iJOBS Simulation: Consulting Case Study

By Juliana Corrêa-Velloso

Among the career paths for STEM PhDs, Life Science Consulting is an attractive possibility for many students and postdocs. However, even amongst the most enthusiasts about this career, the question “what exactly does a consultant do?” can be challenging to answer. On July 7th, iJOBS hosted a workshop led by Sidnee Pinho, Chief Operation Officer of Clearview. Attendees learned about the core skills of a consultant and were guided through a case study. If you are interested in knowing more about this career path, the good news is that as PhD students and postdocs we already have most of the skills needed for Life Science Consulting.

Currently, according to Merriam-Webster dictionary, a consultant is a person who facilitates change and provides subject matter expertise; who offers advice, an expert1. In the pharmaceutical and biotechnology fields, consultants are hired to provide companies with a recommendation about business decisions and market landscape analysis. To get to the solution, consultants usually work in a team, in which they coordinate the strategy, the approach and the communication with the client. This versatility of tasks assignments is one of the main peculiarities of professionals in consulting. Indeed, as Sidnee Pinho explained, in either big management consulting firms or small boutique companies, consultants wear many heats over the life of a project.

Screenshot of Sidnee Pinho’s presentation.

As a project leader or a collaborator in a team, some key responsibilities will always be present throughout a career in consulting. Sidnee Pinho explained how each one of the following duties is important in the daily routine of a consultant:

  1. Problem definer: What is the question the client needs answered? What is the scope of the analysis? At the beginning of the process, it is crucial to understand the client’s needs and define the approach for the solution.
  2. Project manager: Once the project scope is defined, the next step is to develop a work plan. Establishing deadlines, assigning tasks and keeping track of the progress is the backbone of the project.
  3. Data searcher: PhDs are well familiar with the importance of good quality data for a project. Learning how to search for respectable scientific literature and interpret results from the bench is one of the many lessons of a PhD. Similarly, consultants need to collect all the information relevant to the project on which they are assigned. However, rather than a deep and specific analysis typical in academia, the industry requires a different approach. Instead, by doing quick strategic research, consultants become experts in several fields (financial, clinical, basic science, market) necessary to finish the project.
  4. Thought process organizer: Well-designed frameworks are essential to guide the team towards the answer. By being in line with the client’s needs, a good framework helps define the metrics and criteria used in the analysis. 
  5. Quality controller: When working with data, accuracy is critical for credibility. All research should rely on reputable sources and be in a time frame relevant to the project. As expected, validating the results is necessary before taking the next step on the project.
  6. Storyteller: Knowing how to convey a message is a gold-standard skill for any communicator. Depending on the audience, two strategies can be used. Business educated audience with limited time availability requires a “Top-Down” method. A straightforward presentation focused on the conclusion will deliver the expected message. On the other hand, an audience naïve to the subject or with controversial opinions will benefit from a “Bottom-Up” method. By focusing on the key underlying assumption that drove the conclusion, consultants increase their chance of communicating their message.  
  7. Relationship manager: As in any commercial arrangement, client satisfaction requires close attention. Learning how to manage the client is key to most careers in industry.

As a PhD student or postdoc, it is impossible to read all these assignments and not feel that this description is similar to our daily life in the laboratory. Defining a question, establishing a methodology, planning your project, collecting and communicating your results to different audiences (lab meetings presentations, scientific meeting talks, writing papers) and most importantly, managing the relationship with the client, or in this case, colleagues, collaborators and the PI. Through years of gathered experience, STEM PhDs already have most of the transferable skills necessary to pursue a consulting career. Understanding the varied roles required to succeed in this field, STEM PhDs can plan the transition by improving their technical abilities and soft interpersonal skills. With more than 25 years of experience in the Life Sciences industry, Sidnee Pinho shared some advice for future consultants that will help not only in the project execution but also in ensuring that the client expectations are exceeded:

  • To get to the root cause of issues, constantly question everything with the simple question, “But why?”. Be comfortable in asking and answering this question.
  • Ultimately, clients will rely on the consultant for the expert opinion. To feel comfortable in this position, you need to understand the project’s specificities, such as the scientific background, market analysis, competition, business models, financial valuation, etc. In other words, be the “expert.”
  • Do not be afraid of failure.  Every experience is a learning opportunity.

After this helpful and informative overview, attendees were invited to work in teams on a project simulation. The project assignment was as follows:

 “Company X has the opportunity to pursue a long-acting version of prednisone, which is a steroid used to treat morning stiffness associated with rheumatoid arthritis (RA).  Company X has absolutely no experience in the RA market and has no assets in rheumatology generally”.

One complication framed the situation:

“The company has many potential development opportunities and is not sure if they should pursue this long-acting steroid or something else.  They will only pursue this opportunity if they believe they can make $200 million in topline US peak year revenue“.

Groups should provide a recommendation to the following questions:

“Should company X pursue this development opportunity of a long-acting steroid? Calculate the $ opportunity and summarize why or why not in 3 bullet points.”

Attendees were divided into three groups and had one hour to work on the case. The first challenge was to select the necessary information from the extensive supporting material. Groups had access to the RA clinical background, RA prevalence in the US from the past ten years, RA clinical diagnoses criteria, pharmacological alternatives and criteria for steroids treatment, opinions from experts in the field and past and future projections of the RA market. As a PhD, it is difficult to “ignore” data. We tend to look at every piece of information before moving forward on the process. Keeping in mind the advice provided by Sidnee Pinho, the group quickly learned how to select only the relevant information to the case and started debating the possible recommendation.

Surprisingly, after one hour of debating, each group came up with a different revenue number and opposite opinions about the drug launching. Sidnee Pinho explained that rather than the “correct solution,” the structure of the process was more important than the outcome. How did the groups interpret the data? What was the rationale behind the approach? For example, the information about treatment duration and drug dosage per day was missing in the supporting material. Depending on how the groups filled this gap, the outcome was different. As we learned, instead of rushing to get to an answer, it is important to ask critical questions and provide structured and strengthened solutions. In fact, in real life, consultants constantly need to make decisions with limited data and time. In these cases, aiming to understand the problem and getting to the root of it by asking “But why?” helps to provide a structured solution to the client.

This workshop was an excellent opportunity to learn how to tackle a case in Consulting and learn valuable advice from an experienced consultant. As we can see, the parallel between Life Sciences Consulting and a STEM PhD is clear. Students and postdocs will find several opportunities to sharpen their transferable skills during the academic journey and shorten the gap between industry and academia. I invite you to look at your PhD from a new perspective and answer the question: how many of these hats are you wearing already?

References:

  1. “consultant.” Merriam-Webster.com. 2021. https://www.merriam-webster.com (18 May 2021).

This article was edited by Senior Editor Brianna Alexander.

The Logic Model and your project

By Natalie Losada

“A problem well stated is a problem half solved.”

Charles Kettering

Our speaker at this iJOBS event on May 10th was not only a down-to-earth, insightful leader, but he is also a founder of the STEM Advocacy Institute (SAi), a place where you can perfect your Logic Model for your project.  By the end of this article, you’ll understand the Logic Model, how to apply the Model, and where you can practice the Model. 

  • The Logic Model is defined as a theory of change visually linking the connections between the problem, solution, activities, outputs, outcomes, and the intended impact desired by a given program.  It is something that can help you properly plan and understand your projects and life goals.  A schematic outline to help you develop your logic model is shown below.
The Logic Model schematic.  You can use these instructions to help plan your projects in your professional and personal life.

Dr. Fanuel Muindi told the attendees to take screenshot of this model outline and save it, because it should be used time and time again.  When filling out this model template, you need to spend just as much time, if not more time, on shaping and defining the “Problem Space” as you will eventually spend on the “Solution Space.”  Most people don’t spend nearly enough time on this step, and then the solution becomes more difficult to manage or does not address the problem in its entirety.  For example, if you want to be a in Medical Communications in a big pharmaceutical company, is that the “Problem”?  Dr. Muindi answered that the real problem is that you want to communicate science to the public.  Problems and goals should address deeper values and need to be thought out carefully.  You should anticipate framing the problem, reframing it, pitching it to someone, and reframing it.  If your search for the problem looks like the picture below, that’s totally normal.  Dr. Muindi’s path was not a straight and narrow path, but it’s a path he chose that gave him opportunities for the most learning.  Ultimately, his learning experiences helped him understand the problem space.

Screenshot of the Zoom event where Dr. Muindi shows graphically the explorative path of defining the problem that leads to a successful project plan.

The next step is to develop the “Solution Space” or the “how” stage.  If you’ve written a grant or research proposal for your thesis, this is where you explain any instruments, collaborators, or materials you’ll need.  The Logic Model goes one step further and includes the activities you’ll need to complete to accomplish your goal.  Dr. Muindi mentioned that if he enjoys the activities listed in his Logic Model, he knows that the goal is a great fit for him and he’ll stay motivated every step of the way.  Similar to grants proposals, you’ll also need to also explain the “so what?”, which in the Logic Model involves two categories.  The “Outputs” measure productivity and “Outcomes” are changes taking place as a result of your actions.  For example, your output can be a published book and the outcome can be that people read it and are impacted by it!  This could be extended to a paper published during your PhD where your outcome could be that the paper is heavily cited and positively influences your field.  One step that might get overlooked without using the Logic Model is gaging your “Major Assumptions.”  These are things that could put you at a disadvantage if you’re not careful. Any unrealistic assumptions about your abilities, limitations, motivation, or the industry in which you are working need to be carefully considered.  For example, if your goal requires you to be super organized, but you are not, that could cause your dream to fail.  Dr. Muindi suggested using the SWOT analysis to find your “Major Assumptions.”  List out the Strengths, Weaknesses, Opportunities, and Threats that pertain to you and your plan.  For an example of a complete Logic Model, Dr Muindi shared his below.

Screenshot of Dr. Fanuel Muindi’s professional logic model, filled out as an example for the JOBS event attendees to understand how to structure their projects and life goals.

The Logic Model is implemented and cherished at the SAi (STEM Advocacy Institute) founded by Dr. Muindi .  This institute is “an incubator that provides access to research, infrastructure, mentorship, community, training, and funding to accelerate” projects of those who are lacking resources.  These projects can be initiatives, tools, or programs built using the Logic Model.  SAi aims to support underrepresented women and men that have great ideas and great work ethic, but are missing the connection to get their projects into development.  They offer a 10-week SAi Fellows Program to get people started on their project, of which 3 weeks involve framing the question in the best possible way.  Residents of the program attend lectures and spend time beefing up their projects so that by the end, they can pitch their ideas to the public and execute their plans independently.  The demand for their incubator is accelerating so be ready to apply to the next cohort in June (program starts in September)!

To wrap up a perfectly informative event, we had a rapid Q&A session.  There so many insightful questions, but here are just three great highlights from the attendees.

How do you know if your outcome is worth pursuing?

There’s no way to know for certain.  Dr. Muindi stressed that you should be comfortable and willing to go back and change directions.  As everything is a learning experience, your path towards your goal will be very blurry.  Be comfortable with the unknown.

Should logic models be resilient to things like COVID-19?

It’s good to think about what potential threats could detail your plan, because every model has its strengths and weaknesses.  You can think about this is the “Major Assumptions” section as well.  For Dr. Muindi, the pandemic further energized him to follow and abide by his logic model instead of derailing him.  The chaos around the world helped him see that his logic model was the right approach for the goal.  If it wasn’t, he would’ve been further motivated to reframe and replan!

  How do you make the end goal desirable to partners when they are needed long term?

Share your logic as early as possible and make sure your interests are aligned in early stages.  As with any partnership, you need to know each other’s priorities.  Communication is key for any collaboration!

One of Dr. Muindi’s final words of advice should be something that stays in your head every day and ally day.  Ask yourself the hard question – is everything you’re doing making sense for your goal?

Ask yourself the hard question – is everything you’re doing making sense for your goal?

Dr. Fanuel Muindi 

This article was edited by Senior Editor, Samantha Avina.

iJOBS Virtual Site Visit: Merck

Written by: Soumyadipa Das

If you have a background in science and currently looking to work for big pharmaceuticals, then you have opened the perfect article.

On Tuesday, May 4th Rutgers iJOBS arranged a virtual site visit to the pharmaceutical company Merck. One of the world’s major players in the pharmaceutical industry, Merck is a 130-year-old global healthcare company with the mission, “Translate breakthrough biomedical research into meaningful new therapies and vaccines that improve and extend the lives of people worldwide.” Merck operates with 74k employees in 140+ countries worldwide with a heavy focus in biomedical research and development (R&D). In 2020 Merck allocated $13.6 billion in its research and development sector with one-fifth of the total company employees working in the R&D department. Merck has a total of four research sites in US: Kenilworth and Rahway, NJ, West Point, PA, Boston & Cambridge, MA, and South San Francisco, CA. In this virtual event, the attendees were able to interact with the nine current employees of Merck and gain a deeper insight about the company, especially regarding hiring.

Nine Merck employees on the virtual site visit zoom panel represented a diverse cross-section of the company. Seven Panelists were from three distinct sections of the Merck Department of Pharmacokinetics, Pharmacodynamics & Drug Metabolism (PPDM). Drs. Jingjing Guo and Bingming Chen from the Absorption, Distribution, Metabolism, and Elimination (PPDM-ADME) sector, Mr. KJ Lee and Dr. Bernard Choi from Bio analytics (PPDM-BA), and Dr. Xiaowei Zang from Quantitative Pharmacology and Pharmacometrics (PPDM-QP2). Hiring managers Mr. Tom Bateman and Dr. Xiang Yu for PPDM were present as well. Panelists from other Merck departments included Dr. Colena Johnson from Safety Assessment & Laboratory Animal Resources (SALAR) and Dr. Joseph Fantuzzo from Analytical Research and Development (AR&D).

Merck has invested in working together with different departments to ultimately improve life of patients while not restricting themselves to a particular area of biomedical science. Hiring manager Mr. Tom Bateman explained how Merck initially used to work on small molecule-based drugs and has now adapted itself to work with high molecular weight samples ranging from peptides to virus-like particles. Discovery programs in Merck are diverse, 30% of the discovery programs are dedicated to infectious diseases and vaccines, which resulted in the development of V920, a 97% effective vaccine for the Zaire Ebola virus, and granted approval by the FDA in 2019. They also have dedicated 35% of the discovery programs to Immuno-Oncology, resulting the development of popular treatments and drugs like Keytruda, Lynparza, and Lenvima used for treating different kinds of cancer. A chart from Dr. Bateman’s presentation showed that drugs developed by Merck so far took on average 15 years from the early development stage to product approval. With these vast expansions, Merck has a variety of positions to offer people from all fields of STEM ranging from disciplines like mathematics and chemistry to pathology and toxicology.

Panelist Dr. Bingming Chen encouraged potential future applicants to apply for the  Merck post-doctoral program as an excellent way to gain some industry experience while maintaining the good record of publication and presentation.  As an international employee herself, Dr. Chen also explained how Merck provides a welcoming ambiance regarding work visa sponsorship as well. Additionally, most of the Post-doctoral fellows are often offered permanent employment at the company upon post-doc completion. When asked about work-life balance, panelist Dr. Chen replied that she tries to maintain a 9 to 5 schedule but in general work hours are flexible and allows for a well-balanced life for the employee.

As some Merck departments are hiring, panelists described aspects of their Merck departments as they are unique and focus on different biomedical research topics. Dr. Bingming Chen from the PPDM-ADME sector explained how their department deals with investigating exposure of drugs at target site and its engagement. Specifically, ADME specializes in drug optimization and bioanalysis. ADME also utilizes smart trial running to predict dosage use, experimental imaging techniques, and mathematical modelling. Dr. Bernard Choi from PPDM puts an emphasis on analyzing a huge number of samples at low cost and high quality each day through automation which generates a huge volume of data. As a result, Merck is interested in hiring people who will facilitate handling huge amounts of data, a.k.a. people who perform data modelling or machine/deep learning. Representing the SALAR section, Dr. Colena Johnson explained that her department works closely with the regulatory committee for the safety assessment of drug candidates selected in the PPDM-ADME department. In a drug development, SALAR works from the target selection stage, all the way to the post-marketing. Panelist Dr. Joseph Fantuzzo said the AR&D section focuses on small molecules, vaccines, and molecular and material characterization. AR&D characterizes these pharmaceutical developments using analytical techniques including NMR spectroscopy, mass spectrometry, X-ray crystallography, chromatography, imaging, particle analysis, and various immunoassays. When asked about experience requirement for different positions, Mr. Bateman encouraged the fresh PhDs to apply for senior scientist position, while associate principal scientist would need at least five years of experience post PhD in the relevant field.

No matter which STEM background you are from, Merck has something for you. I hope this article gave you some insight about what it is like to be a part of Merck and how their different departments work. I hope you enjoyed reading the article!

This article was edited by Junior Editor, Natalie Losada and Senior Editor, Samantha Avina.

Transition and working in industry: a journey of adaptability and role changes

By: Sally Wang

Academia is perhaps well-known for its stability (if you achieve tenure), continuity, and lifelong dedication to one area of expertise. This is evident in how academic success is defined (being “known for something”) and how academic departments are constructed.  However, Dr. Kenneth Maynard shared at a recent seminar co-hosted by Rutgers iJOBS and The Erdos Institute, a successful industry career often hinges on being adaptable to both people and job functions.

Dr. Maynard—currently a Senior Director of Pharmacovigilance (PV) Affiliate Relations and Global Patient Safety and Evaluation at Takeda Pharmaceuticals—transitioned into industry over twenty years ago. From his first job at Aventis Pharmaceuticals (now Sanofi) as a consultant to his current position, he has navigated eight career transitions and counting. Some of these transitions were uncontrollable (e.g., pharma company shutting down research programs) while others were due to mergers (e.g., when Sanofi acquired Aventis). Yet most transitions were driven by seizing opportunities as they came knocking: moving to a different but higher role within the same company. Dr. Maynard’s industry career trajectory sharply contrasts with the rather linear career path in academia, and it makes navigating the career path in industry seem quite convoluted. That is by design because compared to academic pond, industry is the ocean. Within this ocean, individuals who are adaptable and ready to grab onto opportunities are rewarded. According to Dr. Maynard, the opportunities are there and are plentiful. Perhaps we are wrong to dichotomize the PhD career debate into academia versus industry in the first place, because academia is one career path and industry represents a sea of career paths for PhDs.

For Dr. Maynard, his career progression involves recurrent role changes (e.g., principal scientist/project director/portfolio strategist) that carry different job responsibilities and skill requirements. But all of these positions ask for the same essential skills: adaptability and managing a cross-functional team effectively and professionally. In fact, collaborating with and managing people are important skills in any field including academia, but they are often not emphasized enough. As PhDs, we are trained to learn technical skills—be it a new technique or decoding a new regulatory document—but people skills are oftentimes left on the backburner. Why should they be that important when you’re surrounding by like-minded, similarly-trained people in academia? Good luck finding that in industry meeting rooms. So for those aspiring industry scientist and leaders, it is critical that you can work with and lead people of all backgrounds and expertise if you want to be successful.

One important tip from Dr. Maynard is to be strategic and intentional about your career.

In addition to developing and honing people skills, what else should PhDs consider when transitioning into industry? One important tip from Dr. Maynard is to be strategic and intentional about your career. Timing is very important in any transition, be it from academia to industry or within industry, or perhaps even industry back to academia (yes, that is a career path). One of the ageless questions related to timing is “should I do a post-doc?” Well, it depends on individual career goals and plans. If a post-doc can expose you to a new subfield with techniques and tools (e.g., machine learning or deep learning) that will woo industry hiring managers, then perhaps pursuing a short post-doc is on the table. Even the opportunity to write a grant (which seems to be a skillset endemic to academia) can show prospective industry employers that you have vital skills such as strategic thinking, effective communication with stakeholders and budget-planning insights. But on the other hand, if you’re contemplating career trajectories that emphasize on-the-job experiences (e.g., medical writer or user experience researcher), then a PhD has probably more than prepared you to transition right out of graduate school.

The takeaway is: any career transition requires some level of introspection and knowing what and when works best for you. Be prepared to adapt and be flexible throughout this journey. The key is to make career transitions work for you rather than you working for them. Insights from folks who have done it before you are valuable, but it is ultimately your own journey that is set by your personality, skillsets, interests, motivations and the impacts you want to have. Just as knowledge without implementation is useless, career transition without adaptability is impractical.

This article was edited by Senior Editors Helena Mello and Samantha Avina.

iJOBS Career Panel: Scientist Positions in BioPharma

By Gina Sanchez

PhD candidates often concern themselves over what a job in industry might look like. Images of high-tech labs are both exciting and frightening as a new adventure awaits. The reference for many of our experiences in industry are large pharmaceutical companies, such as Merck or Pfizer, or sometimes start-ups. Samantha Avina recently wrote an article detailing an Industry Career Panel hosted by iJOBS as well. But we often do not consider the mid-sized companies. As more PhDs begin to choose a career outside of academia, we must begin to expand our horizons. Luckily, iJOBS hosted a career panel of Rutgers alumni who now work in mid-sized pharmaceutical companies in order to broaden our scope for our upcoming career searches.

Trajectory of PhD students in 2015, Ryan Raver of
http://thegradstudentway.com/blog/?p=2153

The panel began with short introductions by our four panelists:  Dr. Csanad Gurdon, Senior Scientist at AeroFarms; Dr. Eric Himelman, Non-Clinical Research Scientist at Ultragenyx Pharmaceuticals; Dr. Praveen Bommareddy, Associate Director of Research at Replimune; and Dr. Jimin Zhang, Assistant Principal Scientist at Insmed.


For the first topic, the panelists discussed how they got into their current career. Dr. Gurdon had done both his PhD and post-doctoral fellowship at Rutgers University, and began the application process at the end of his post-doc. He knew that he enjoyed teaching, but did not want to write grants, and that he did have most of the qualifications for a senior scientist, a subject-matter expert generally with a graduate degree that oversees research and develops projects. In this position he still writes grants, but mostly Small Business Innovation Research (SBIR) grants. Additionally, he credited SciPhD for many of his transferrable business skills, which are critical for small-to-mid-size pharmaceutical companies. Such skills include project management, communication/ability to work within a team, and effective time management. Dr. Himelman was a bit different in that he chose a company that would essentially allow him to continue working on the topic that he had focused on during both his PhD and post-doctoral fellowship at Rutgers, muscular dystrophy. It is worth mentioning that he applied during COVID-19 and is already in a leadership position making hiring decisions for his team. Dr. Bommareddy utilized the iJOBS program during his PhD at Rutgers to do an internship at Regeneron, gaining experience in a larger pharmaceutical company. He realized that the idea of watching a company grow from within was something that he wanted, which led him to join Replimune, which is a now clinical-stage biotech company. Dr. Zhang completed his PhD at Rutgers and began his post-doctoral training shortly afterwards, but due to a funding issue, decided to move to an industry post-doc at Insmed. As with many post-doctoral fellowships in industry, he was told that the training would be for one year and upon success, he could be offered a permanent position. As demonstrated by the panelists, there are many ways to transition into a mid-sized industry company.

A graphical listing of various transferrable skills in the workplace, courtesy of https://mrsimon.ai/transferable-skills-and-your-next-job/

You may be wondering what would be expected of you in a mid-sized company compared to a larger one. Dr. Gurdon first advised that neither is better than the other, but rather, it is up to the individual. He emphasized that it is essential to “note your transferrable skills,” such as project management. We have all been sub-consciously honing our project management abilities just by working on our theses for the past several years! This is essential in any job that you may choose after completing your PhD, but it is critical in a mid-size company. Dr. Himelman and Dr. Bommareddy were able to speak to the importance of project management, as they now manage several teams and must coordinate between them. Dr. Himelman is currently in the transitionary phase as he is building his own team; so similar to a new PI, he is mentoring new scientists so that they can help optimize techniques and teach future members, allowing him to work more on the business side of things. On the other hand, Dr. Bommareddy must coordinate between pre-clinical translational and clinical translational teams – including 15 distinct labs – to compile results and assess feasibility between groups. Dr. Zhang also chimed in, mentioning that “in a small company, you must wear many hats” at the same time in order to effectively do your job.


Finally, the panelists shared some over-arching application advice. First, Dr. Gurdon advocated for doing informational interviews with companies, especially if you are looking to obtain as much information as you can about a title or field. He also advised using your network to your advantage when applying for a job. Dr. Himelman warned us that interviewers are likely to ask you to explain various skills that you note in your resumé to make sure that you actually understand the technique/skill. He also said that you should ask questions that get at the experiences of the interviewer so that you can connect on a more personal level. When he is interviewing candidates, he stated that he looks for someone who he could get along with (since you will be around each other 40+ hours every week) and that he likes people who ask questions, demonstrating that the individual is not just complacent with what they already know. Importantly, he stated that you do not need to have every skill that the employers want, just emphasize what you do know and do not approach it as a deficit. Overall, the panelists agreed that you should tell the recruiter what you will contribute, instead of asking them what you can contribute.


From this panel, students were able to gain valuable insight into what it is like to work in a mid-sized pharmaceutical company. Employees have more creative freedom and often have to wear more hats in a mid-sized company compared to one of the larger pharmaceutical companies. Many skills that we learn during our PhD and post-doctoral training would leave us well-suited for this career path.


This article was edited by Junior Editor Zachary Fritz and Senior Editor Brianna Alexander.

Leadership Skills and How to Be an Inclusive Leader

By Juliana Corrêa-Velloso

Graduate students and postdocs operate on both sides of leadership. As students they are mentees, but for newer and junior level lab members they serve as mentors. Although these key interactions are as important as the technical skills acquired during the PhD, they are often neglected. Looking back at your PhD training do you remember being prepared to be a leader? More importantly, do you recognize yourself as a potential inclusive leader? On February 11, iJOBS hosted the How to be an Inclusive Leader seminar led by Dr. Srikant Iyer, director of the Science Alliance program at The New York Academy of Sciences. Dr. Iyer shared his knowledge on the leadership skills for scientists and offered some directions of how to be a mindful and inclusive leader.

Most PhD students learn interpersonal skill management based predominantly on personal experiences and environment around them. I am sure many of us, at some point of our career have doubted our leadership capacity. Negative feelings toward leadership positions such as “I am the wrong person for this” or “I can’t do that” can unfortunately be common recurring thoughts. To change perspective of these thoughts, Dr. Iyer encouraged the participants to reflect on what motivated them to apply for a graduate program as his introduction to the seminar. “What were the skills that made you a good candidate to the program? And why did you select that specific program?”, Dr. Iyer pressed. Possessing traits like curiosity, enthusiasm, and strong communications skills, set PhD students on a strong leadership development pathway, he explained. And if you believe that your record with leadership experiences is not ideal, no worries. According to Dr. Iyer leadership traits can be developed and trained. But how?

During the seminar, attendees were asked to describe what being a leader meant to them. The audience generated a word cloud that showed words describing leadership characteristics such as “mentor, inspirational, support, role model, motivator and communicator”. Indeed, these characteristics matched with the classic figure of a mentor, a crucial player in the academic formation. PIs and senior postdocs do provide some guidance to PhDs students and junior postdocs on leadership development based on their own experiences.  However, this important task should not rely solely on personal experiences or common-sense knowledge about a specific matter. Without structured science-based orientation about their leadership approach, mentors can be misled by implicit bias. To build up a new generation of inclusive leaders we need to look back at how current leaders were shaped.

Figure 1: Screenshot from Dr. Iyer seminar. Attendees were asked to define what does a leader means to them. The image represents the word cloud formed by the answers.

Based on a study from American Psychologist Journal1, Dr. Iyer described the status of the leadership culture in the United States in the early nineties. This study showed that rather than being chosen based on leadership skills, first line supervisors were chosen based on job-related technical skills with a correlated 60%-75% rating of poor managerial competence. The study also highlighted the importance of the feedback from subordinates, peers, and superiors to the evaluation of the leader. Considering the importance of a leader for the functionality of an organization, the consequences of working with unfitted and untrained leaders can go beyond the dysfunctional interaction in a group to affect the whole system. Ultimately, investment in all parties involved in that alliance, at both the individual and organizational level is critical to achieve a constructive and beneficial work environment.

One of the main characteristics of a compassionate leader is equity.  In terms of a group, equity means equal opportunities while equality means equal resources. Diversity comes from the inclusion of underrepresented groups. Diversity equity, therefore, is equal opportunity for these groups. It is impossible to talk about equity without thinking about inclusion. As Dr. Iyer explained, considering team gender, ethnicity, disabilities, LGBTQIQA+, and different cultural backgrounds is essential to a healthy leadership culture. He also highlighted the importance of understanding some relevant theoretical concepts to have fruitful discussion about inclusion and representation in the workplace. Dr. Iyer showed a practical example formulated by Dr. Robert Seller, Chief Diversity Officer at University of Michigan to emphasize his point. “Imagine that a dance party is being organized. In that context, diversity happens when everyone is invited to the party. Equity will happen if everyone gets to contribute to the playlist. Inclusion is allowing everyone the opportunity to dance”, he said. This good example shows that the assimilation of these key concepts is essential not only for a throwing a good party, but also for nurturing a prosperous workplace environment (please, wait for the pandemic to be over to put that example in practice!).

Awareness about others is key to inclusive leadership. A longitudinal university-wide study2 showed that STEM classes taught by “fixed mindset” faculty have larger racial achievement gaps and inspire less student motivation compared to classes taught by faculty with a “growth mindset”. According to the study, faculty mindset beliefs predicted student achievement and motivation above and beyond any other faculty characteristic, including their gender, race/ethnicity, age, teaching experience, or tenure status. In fact, implicit biases related to gender, race, and culture, are deeply rooted in society and substantial barriers faced by underrepresented groups. Another important aspect is the quality of the communication of a leader. According to a Harvard Business Review3, men are more often positively described in their performance reviews compared to their female counterparts. The impact of these canonical leadership shortcomings can affect all steps in career development. To overcome these problems, educational initiatives and implementation of inclusive policies are necessary to start and guarantee needed leadership inclusivity changes. From this perspective, it is clear that key representatives within an organization, and the organization itself, need to be responsible for identifying and creating policies in preventing these biases.

An inspirational initiative cited by Dr. Iyer, is The Open Chemistry Collaborative in Diversity Equity (OXIDE). By hosting equity workshops and conducting demographic assessments, OXIDE contributes to the reducing of inequitable policies and practices that have historically led to disproportionate representation on academic faculties with respect to gender, race-ethnicity, disabilities, and sexual orientation. As a result of this kind of initiative, additional inclusive practices are being discussed across universities. For example, the traditional “wall of fame” from most university departments are not representative and diverse, reinforcing a single type of stereotype. With the promotion of inspirational examples from all demographic groups, universities can find a balance between celebrating the past without jeopardizing the future. By encouraging personal growth and advocating for inclusive policies, best practices of leadership will evolve. Individuals need to be allowed to be themselves to achieve their full potential and bring to the table their unique contribution. As Dr. Iyer explained, by covering their personal identity to fit into a professional identity, the potential for innovation, creativity, and success is downplayed.

By encouraging personal growth and advocating for inclusive policies, best practices of leadership will evolve. Individuals need to be allowed to be themselves to achieve their full potential and bring to the table their unique contribution.

– Dr. Srikant Iyer

Now that we have an idea about how a nurturing workplace environment should be, how can you prepare yourself to be an inclusive leader? Here are some final takeaways from Dr. Iyer’s seminar:

  1. List your strengths and values and keep the list close to you. As a STEM PhD, you certainly already have several leadership skills.
  2. Remember, as any other skill, leadership skills are acquired. Be intentional about your goals and training.
  3. Have an open-minded approach and educate yourself about equity, diversity, and inclusion. In the process, be gentle with yourself and with others. Be mindful that each one has a diverse cultural background and different levels of literacy on these subjects. That is why engaging in educational initiatives are important.
  4. As a woman, if you struggle with Impostor Syndrome, remember that you are in an environment that suffers from a lack of inequity. Good materials to read are the paper Impostor Syndrome: Treat the Cause, not the Symptom by Mullangi and Jagsi4 and the study report: Sexual Harassment of Women: Climate, Culture, and Consequences in Academic Sciences, Engineering, and Medicine5.
  5. During the seminar, Dr. Iyer and the attendees discussed 2 case-studies related to equity, diversity, and inclusion in the academic set-up. I strongly recommend readers view the recording of the event at the iJOBS events page and enjoy the constructive discussion about the cases.

I hope this article helps you to envision yourself as an inclusive leader and inspires you to nourish your colleague’s growth along the pathway.

References:

  1. Robert Hogan, Gordon Curphy, Joyce Hogan. “What We Know About Leadership Effectiveness and Personality”, American Psychologist, 1994.
  2. Elizabeth Canning, Katherine Muenks, Dorainne Green, Mary C. Murphy. “STEM faculty who believe ability is fixed have larger racial achievement gaps and inspire less student motivation in their classes”, Science Advances, 2019.
  3. David Smith, Judith Rosenstein, Margaret Nikolov. “The Different Words We Use to Describe Male and Female Leaders”, Harvard Business Reviews, 2018.
  4. Samyukta Mullangi, Reshma Jagsi. “Imposter Syndrome, Treat the Cause, Not the Symptom”. JAMA, 2019.
  5. National Academies of Sciences, Engineering, and Medicine; Policy and Global Affairs; Committee on Women in Science, Engineering, and Medicine; Committee on the Impacts of Sexual Harassment in Academia; Paula A. Johnson, Sheila E. Widnall, and Frazier F. Benya, Editors. “Sexual Harassment of Women: Climate, Culture, and Consequences in Academic Sciences, Engineering, and Medicine”, 2018.

This article was edited by Senior Editor Samantha Avina.

Virtual Career Panel: Postdocs in Industry

By Zachary Fritz

When you hear the term “postdoc”, do you think of a recent PhD graduate, diligently working in an academic research lab with the hopes of one day becoming a tenured professor? Were you aware that a postdoc could be a stepping stone to a career as a scientist in the pharmaceutical and biotech industries? On February 5th, the Rutgers iJOBS Program hosted a virtual panel with four RU alumni to help demystify this often overlooked route. The event’s panelists explained the typical interview process for an industrial postdoc, outlined the job expectations at each of their companies, and gave some career preparation advice. Each panelist came from a different company and emphasized that a postdoc in industry is one of the best starting positions if you desire a career in hands-on research at a pharmaceutical or biotech company.

Bristol Myers Squibb, Merck, Regeneron, and Eli Lilly all had postdocs on the panel of iJOBS’ February 5th event.

Bristol Myers Squibb, Regeneron, Merck, and Eli Lilly were represented at the event by Dr. Kubra Karagoz, Dr. Aysegul Guvenek, Dr. Yixiao Zhang, and Dr. Anchal Sharma, respectively. All panelists had been in their current positions for a year or less, though both Drs. Karagoz and Sharma did academic postdocs at the Rutgers Cancer Institute of New Jersey (CINJ) prior to being hired by their companies. While their companies and areas of study differed, the panelists shared many experiences, including coming from international student backgrounds and undergoing very similar application and hiring processes. The panelists explained this typical interview process at the start of the event, while further questions and virtual breakout rooms fostered discussion about job roles/expectations, company culture, and additional career advice.

Applying and Interviewing

After the introductions, the panelists quickly addressed a hot topic for graduate students on a job hunt: what was your interview process like? The answers were surprisingly consistent. If a company is interested in your application, your phase one interview is typically a 30-45 minute phone interview where you can introduce yourself and talk a bit about your scientific background. Phase two is usually a presentation to the entire research team of your prospective lab (previously on-site but now often virtual due to COVID-19) of your doctoral thesis work, with a Q&A session at the end. For the final phase, it is common to have many (eight or more) one on one interviews with various members of the team. The panelists stressed that most of the questions asked in these interviews were technical, i.e. focusing on their scientific background, skillset, and problem-solving abilities, though Drs. Zhang and Sharma recalled some behavioral/personality focused questions. Dr. Zhang emphasized that during both your presentation and individual interviews you should find some way to tie in any research experience you have with your prospective lab’s work. His own doctoral research in the Rutgers Chemistry department at first seemed to be a far cry from the work he would eventually be doing at Merck’s Upstream Bioprocesses. However, Dr. Zhang realized he did have some valuable experience in mammalian cell culture and other biology-based skills, and made sure to mention this during his presentation and interviews.   

The most important point, Dr. Sharma noted, is to thoroughly read the postdoc job description and tailor your resume and/or CV to the job requirements.

Sometimes just landing a phone interview can prove extremely competitive, so the panelists had some tips for making your application stand out. The most important point, Dr. Sharma noted, is to thoroughly read the postdoc job description and tailor your resume and/or CV to the job requirements. Be sure to use keywords, which are the specific skills and knowledge areas one would need regularly for the position. Dr. Guvenek noted the importance of a complete LinkedIn profile, so be on the lookout for relevant iJOBS events or sign up for a virtual LinkedIn or resume critique session with the Rutgers Office of Career Exploration and Success. Additionally, Dr. Zhang acknowledged that having an industrial internship on your resume is particularly attractive to recruiters. Surprisingly only one of the panelists, Dr. Guvenek, utilized her network of contacts to help her secure an application and interview opportunity. . It goes to show that while networking may not be the end-all-be-all in this field, it still helps to look for possible connections everywhere!

After You’ve Been Hired

You might be asking yourself, what can I expect as a postdoc in industry? Generally, a few things hold true across all industry postdoc programs: the positions last 1-4 years, they are focused on basic research (i.e. not on products that are already in the development pipeline), and yield at least one publication.  The starting salaries ($70-$75k) are usually $10-$15k higher than an academic postdoc. That being said, the structures of postdoc programs can vary quite a bit between companies. In some cases, as with Dr. Karagoz, you’ll be in charge of selecting and writing your own project proposal (with some input from mentors), while other companies pair you with a mentor who will already have a project in mind or a list of ideas for you to choose from. Depending on the company, side projects and collaborations can also be possible, though the panelists emphasized that publishing the results of your main project is of paramount importance. There often isn’t any formal onboarding process, but all of the postdocs assured that training is readily available for techniques you may not be familiar with. In fact, all of the panelists touted their companies as very supportive in terms of learning new skills, providing benefits like maternity leave, and visa sponsorship for international students.

One critical question was left for the end of the event: is a postdoc absolutely necessary if you want to pursue biomedical research in industry? While the panelists noted that it was common for well-performing postdocs to be hired as permanent scientists by their companies, a postdoc as a prerequisite might vary by field and company. Dr Guvenek, for example, felt that in her field of bioinformatics there are plenty of industry jobs that don’t require a prior postdoc, whereas Dr. Sharma thought that such jobs were rare in her field. Dr. Karagoz also pointed out that her company, BMS, typically only hires PhDs without postdoc experience as contractors. It is also possible that if you apply for a permanent scientist position without quite enough relevant experience, a company may still hire you as a postdoc with the intention of building up your skillset.

Even in the midst of a worldwide pandemic, pharmaceutical and biotech companies (including all the ones represented at this event) are still hiring. Whether you decide to apply as a postdoc, contractor, or try your luck with a permanent scientist position, it’s time to polish up your resume, practice your interviewing skills, and get your name out there!

This article was edited by Junior Editor Natalie Losada and Senior Editor Samantha Avina.

Get Organized: Data Carpentry Workshop for Genomics Analysis

By Natalie Losada

Image of script for processing genomics data, with images of DNA (large circle) and cells under a microscope (small circle).

Computing and bench work might feel like two different worlds. But much like other dualisms—sun and moon, sound and silence—they are complementary, and each necessary.  By uniting both computational and experimental research, you can make your research more impactful.  Most of us are already skilled at the bench, so the Data Carpentry community sought out to fill in the gaps of basic computing skills for researchers.

The Genomics Data Carpentry workshop was held from January 26-29, 2021, and taught by instructors (this link has a full list of instructors’ Twitters, ORCIDs, and GitHub links!) and helpers from all over the globe.  It included researchers from Italy (Monah Abou Alezz – postdoctoral fellow, San Raffaele Hospital), Poland (Aleksander Jankowski – Assistant Professor, University of Warsaw), and the United Kingdom (Vasilis Lenis – lecturer, Teesside University, and Nadine Bestard – University of Edinburgh).  The Data Carpentry community was phenomenal at explaining and documenting every lesson, so I recommend you take a look at their website to learn at your own pace.


Screenshot of workshop – slide from Monah Abou Alezz’s presentation.   Featuring Janet Alder in top-right, Monah just below, and other instructors and helps for the workshop.

The workshop was predominantly a hands-on experience.  But rightfully so, it started out with a word to the wise. If you take nothing else from this article, take away this lesson: the importance of pre-project organization and management.

“Think like computers when you are using spreadsheets: they cannot separate your arbitrary sample names like you can.”

– Monah Abou Alezz

Organizing data can easily be discouraging for people, but it doesn’t have to be if we think about it strategically before generating any data.  Your metadata should come first, which is essentially “the data about the data”.  For example, your DNA sequences are data, but your metadata includes what organism it comes from, the particular strain, any known mutants, sample concentration, and much more.  Researchers normally keep a written lab notebook with experimental details, but Monah insisted on keeping an electronic spreadsheet (Microsoft Excel, LibreOffice Calc, Google Sheets) with metadata and data for all experiments.  He also emphasized “data tidiness”.  If you want to automate data analysis, you need to have spreadsheets that are easy for computers to understand.  It helps to keep in mind that computers are only able to detect the presence or absence of something.  A sample called “green_monster” is not the same as “green_MONSTER” because the computer is detecting the presence or absence of capital letters.  For all spreadsheet preparation, you should follow these notes:

  • Avoid spaces in column, row, and sample names
    • Use underscores and dashes. In coding, spaces are required for some commands to separate the action from the sample.  If the sample name has two words, the computer will only identify the first.
  • Keep single types of information in each column
    • If a column has numerical and alphabetic entries, or just if the column seems crowded, do not be afraid to add more columns. This will keep all sample information neatly organized and easily interpretable by the computer.
  • Use consistent formatting
    • Capitalization, format of dates, decimal accuracy, abbreviations, sequential labeling, all need to be the same. So, decide on your favorite format before filling the spreadsheet and stick with it.
  • ALWAYS keep the RAW data untouched
    • Copy data to a separate folder where you can work with it (i.e., a “working directory”). If you find something wrong after processing the data, or decide to try methods of analysis, you can still go back to the original, untouched file.
  • Stick with CSV and TSV formats
    • CSV and TSV, Comma and Tab Separated Values, respectively, are text files that can be read by programming software, while Xlsx files cannot.  CSV/TSV files are also smaller and therefore better when working with copious volumes of sequencing data.

Other important considerations (when automating your data analysis)

If you plan on doing Next-Generation Sequencing (NGS) projects for whole genomes of multiple organisms or samples, you might run into some challenges away from the bench.  Sequencing files can be quite large.  They usually include the genome and quality of each sequence read, and sometimes they are “paired” and contain overlapping reads of the same segment. Therefore, storing raw and working data separately might be cumbersome on a personal computer with limited storage.  Ask yourself, can your computer handle the work? Or do you need a cloud service?  As a Rutgers student, you have access to Box for storage.  Resources like Dropbox or Google Drive might not have enough storage at an affordable rate for all your data, so look around to find appropriate solutions for your projects!  You also need to confirm that your files can be accessed by other lab members and that they are in a redundantly backed up location.  The recommendation is to back up data thrice: once in a cloud (like Amazon S3, Microsoft Azure, Google Cloud) and on two hard drives in geographically separated locations (like your lab hard drive and campus data storage facility).

Ask yourself, can [your computer] handle the work? Or do you need a cloud service?

Once you know where your data will be stored, you need to plan where it will be processed.  Again, can your computer handle it? Or will you need the aid of a high-performance computing center like the Amarel Cluster, which is also available for Rutgers students.  The Office of Advanced Research Computing (OARC) are extremely helpful in getting you set up and very responsive should you have any questions while processing your data.

You should also plan ahead to know what programs you will use to process your data.  Some software are written in particular languages and can only read certain formats of files.  You need a plan for the proper transferring and copying of data to avoid small errors.  When you receive your sequencing results, they will be in compressed/zipped files, so unzipping before transferring could result in lost or altered files.  If copied with the incorrect software, the format of files could be unintentionally changed and rendered unreadable by some programs. To prevent errors, ensure your samples are properly labelled and logically organized.  As mentioned in the previous section, keep all formatting consistent. Label your samples in sequential order so it’s easy for everyone involved, watch out for repeat sample names or barcodes, and make sure you have enough, but not too many columns in the spreadsheet you send for the NGS order.  This spreadsheet is different from your own records with the detailed metadata and data, which you will acquire and input after the sequencing results are returned to you.

Once your experiments are completed and ready to be published, you will be working with publicly accessible databases.  Two common databases are SRA (Sequence Read Archive) from NCBI (N­ational Center for Biotechnology Information) and ENA (European Nucleotide Archive) from the EBI (European Bioinformatics Institute).  You need your raw data to be easily accessible and manageable when transferring to these archives.  And, if you’re ever looking for raw data from a particular organism or project, much like searching scientific literature or reading news in this day and age, check all sources.  Some places might have information that others don’t!

Beginning to code

The majority of the work involved with computing is learning the computer language for the programs you want to use.  An article about everything I learned in this workshop would end up as a long grocery list of commands and notations. With that in mind, I will only discuss the basics needed to start automating your data analysis and some tips to help you learn more quickly. 

First, if you’re new to genomics data carpentry, you might want to work with some practice data.  We obtained our practice data from NCBI.  Our practice data came from a project named Bioproject, which you can search for on the NCBI website with the accession number PRJNA294072.  We clicked on the “Long-Term Evolution Experiment with E. coli” in the search results, and then clicked on the total number of SRA experiments.  This brought us to a huge list of sub-projects (from here you can click “send results to Run Selector” so they are easier to look through).  For more detailed instructions on how to download the data, please check out Data Carpentry’s  website.  Some databases -such as SRA- require a toolkit to download data from, but others -such as ENA- do not.

Screenshot from the Data Carpentry website showing the input command to view the top few lines of a sequencing results file. Below the command shows the output which includes metadata in the first line, the sequence in the second line, metadata again in the third line, and the quality of the read using symbol representations.

To actually begin coding and communicating with the computer, you need two things: an interface that allows communication with the computer, called the command line interface (CLI), and a secure way to transfer large files.  If you have a Linux or Mac operating system, you’ll have a command line ready to use, called the terminal.  If you are a PC user, you will need to download the software PuTTY, which is a terminal emulator.  As a Linux or Mac user, you should be able to transfer files from a remote server to your computer, and vice versa, using the command ssh (secure shell), which is a cryptographic network protocol.  For PC users, you can download WinSCP (Windows Secure Copy Protocol), to securely transfer files between your computer and a remote server.  Alternatively, a PC user could download MobaXTerm that performs the functions of both PuTTY and WinSCP, but you should ultimately choose the software whose interface you understand better.

Tips for learning:

  • Spaces are not the absence of information
    • Spaces are purposefully placed in commands to separate “arguments” (a math term for a piece of information).  A space means “okay, I am done naming my file, now here is the next command/piece of information”.
  • Look at what the letters mean
    • Example: sometimes the command for a certain software will include a “-o” followed by an instruction to give the final output name.  You can easily remember this as “-o” stands for “output file name”.
  • Name files and folders accurately
    • Think of this like chess, you need to plan five steps ahead when naming.  You can’t just name folders “data”, “backup”, “extra”.  If you have to redo everything and you have new data and new backup files, how will you differentiate which is which? Be descriptive, but concise.

Keep in mind that it will take a few tries to get your workflow to be efficient.  The irony of automating data analysis to save time is that you spend more time in the beginning trouble shooting before you can actually reap the time saving benefits.  Also keep in mind that at least half, likely more, of the time spent coding is actually spent googling.  The Data Carpentry instructors all agreed on this point, because you will need to spend time learning what programs you can use to analyze genomics data, understanding how the commands work, finding how to connect commands into a single script, and discovering where you usually make your syntax mistakes (of which there will be plenty, it happens to everyone).  Ultimately, the best way to learn to code for data analysis is having a problem and trying to solve it.  If you don’t already have a problem, find one online and start computing!

This article was edited by Senior Editor Helena Mello and Senior Editor Samantha Avina.

Life as an industry scientist at a biotech company

By: Sally Wang

One of the most common questions that newly minted PhDs ask when considering non-academic jobs is about the transferability of their skills and knowledge. Given that most PhDs only know one type of research environment, any non-academic venture would appear to be a large departure from the research ecosystem that they are acclimatized to during graduate school. However, those looking to transition into the biotech sector may be pleasantly surprised at how much their skills and knowledge are transferable.

At this iJOBS event, Eduardo Perez PhD, a 2006 Rutgers grad in Biochemistry and Molecular Biology, shared his experiences working for a small biotech company. Perez started at Signum Biosciences as a postdoctoral research scientist on a fellowship sponsored by the NJ Commission on Science and Technology and has worked for the company for 14 years and counting. Over the years, he has ascended the career ladder and is now serving as the company’s chief scientific officer (CSO). Perez described small biotech companies such as Signum as “an organized chaos” and that working for them comes with a lot of room for innovation and ability to make an impact on the company’s scientific mission. According to Perez, industry scientists who work for a biotech company typically have three types of job responsibilities in support of the company’s overarching mission.

First, scientists play a role in the company’s business development where they work with the CFO/CEO on strategic initiatives and pitch science to interested companies or investors. They also take part in endeavors such as attending conferences to network and dealing with technology commercialization. Much like exploring funding opportunities and pitching ideas to program officers at grant-funding agencies, this aspect of the industry scientist position is very much about strategically mapping out research aims and garnering interest from potential funders — something that academic scientists are well-versed in. Even technology commercialization is not something far removed from academic research, so much so that many research institutions including Rutgers have an office of technology transfer that facilitates such initiatives. As such, PhDs can be certain that their training in graduate school and postdoc will prepare them well to wear this hat as an industry scientist. 

Second, scientists working in biotech contribute to the research and development (R & D) operation of the company. Here, typical duties include performing data analysis, project prioritization, budget allocation, and serving as the point person with collaborators and consultants. These duties are well within the comfort zones of PhD grads and engages similar skillsets that have been honed throughout graduate school and postdoctoral training. Although academic and industry research often diverge in their research objectives, PhDs should not have much problem adapting their lab expertise in academia to pursue more industry-oriented projects. 

Third, scientists at a biotech company are involved in medical writing and marketing. The writing aspect comprises tasks such as writing grants and drafting manuscript for peer review whereas the marketing aspect involves making presentations for non-scientists, managing website contents and social media presence. This is another aspect of the industry scientist’s job that a transitioning academic PhD will feel right at home with. Particularly in recent years, academia itself has seen a surge in social media presence where a lot of academics including graduate students maintain an active online profile whether it is via lab or personal websites or Twitter. As such, PhDs transitioning into industry should be no stranger to the world of industry marketing whose goal is to engage the public on the latest scientific advancements.

A small biotech company offers the big-fish-in-a-small-pond environment whereas a big pharma company offers the small-fish-in-a-big-pond environment—something that PhDs looking to enter the biotech sector should consider.

In addition to sharing the types of work that an industry scientist does at a biotech company, Perez also shared his views on how small biotech companies compare to large pharmaceutical companies. According to Perez, while large pharmaceutical firms offer more job security that is coupled to a tried-and-true and structured work environment, small biotech companies could provide more job satisfaction that allows the scientist to be more directly involved in planning and implementing the company’s big-picture goals. In other words, a small biotech company offers the big-fish-in-a-small-pond environment whereas a big pharma company offers the small-fish-in-a-big-pond environment—something that PhDs looking to enter the biotech sector should consider.

As you peel away all the industry jargon of CDAs (confidential disclosure agreements) and MTAs (material transfer agreements), the role of an industry scientist at a biotech company is perhaps more similar than different to that of an academic scientist. Whether science is conducted in an academic or industry environment, the ultimate goal is one and the same: leveraging scientific research to solve a problem. What differs will be the application of the solution—where industry tends to be more product-driven whereas academia is more knowledge-driven. However, what is important to take away is that an academically trained scientist has the skills and toolset to successfully transition into the biotech industry.

This article was edited by Junior Editor Gina Sanchez and Senior Editor Samantha Avina.

Developing Leadership and Business Skills for Scientists: SciPhD Virtual Workshop 2021 – Part 2

Written by Shawn Rumrill

Day 3 – Developing your people

Welcome back to part 2 of the Rutgers iJOBS SciPhD Leadership and Business Skills for Scientists workshop! If you missed part 1 you can check it out here. Over 20 hours and across 5 days, this program challenged participants to develop soft skills that complement and enhance their technical abilities, enabling them to effectively land industry-focused jobs after their PhD. Part 1 of the workshop was about building and identifying your skills to construct a targeted resume and how you can land an interview. This was followed by a session on skills to help you nail the interview. Part 2 focuses on more soft skills that will help you be the most effective in getting your dream job. Continue reading to learn how the SciPhD workshop helped attendees to develop strong interpersonal communication skills, network, build business and project management skills, and negotiate.  

Arguably, the best way to be productive and effective is to establish productivity and efficiency in your team while encouraging their own success, which in turn helps you to become successful in the workplace. But how do we become both good leaders and simultaneously good team members? Kicking off the first session of the SciPhD workshop part 2 was SciPhD co-founder Larry Petcovic, who introduced what they’ve identified as 3 key components to interacting with other’s and how this impacts their desire to work. 

The first of these is mastery or the idea of becoming an expert. People are motivated to work by a desire to themselves become better or the expert, so leaders should promote this. Instilling a sense of pride in someone often makes them more receptive to input and careful about their own work. In turn, this also provides opportunities for individuals to become their own expert and mentor others, which is rewarding.

The next key component in interacting with others is the concept of autonomy. Mr. Petcovic notes that, to some extent, everyone likes to be self-directed. Just as we all would rather choose what we have for lunch and decide when we want to start tackling a project in academia, this concept translates to industry and professional careers as well. A sense of autonomy can also provide confidence, which may help individuals do their jobs more efficiently and effectively. Finally, allowing people autonomy in their work also allows for them to be successful and promotes a sense of accomplishment and pride.

Last but not least, the third component is a sense of purpose. This is the big why question that we each undoubtedly ask ourselves many times a day. Why am I doing this experiment? Why am I pursuing a PhD? Why am I interested in contributing to this field of science? Fortunately, there are many answers to this question, and each is different from the next. Often, people are driven by a team focus, a common vision in outcomes, or just by a need for productivity. No matter the reason, a sense of purpose is an important driver in determining a person’s drive to work and do their job well. On this note, mastery, autonomy, and purpose, are important factors that leaders and team members alike should promote within each other whenever they interact to help drive productivity, efficiency, and a sense of happiness in the workplace. 

It is important to promote various aspects of people’s desire to work when developing your team and relationships, but even more so on an individual basis. The workshop next focused on how individuals develop mastery in their work. Mastery was conceptualized as 4 defined phases of competency. 

  • Unconscious incompetence: not knowing what you don’t know
  • Conscious incompetence: knowing what you don’t know
  • Conscious competence: you know how to do a task but it takes effort
  • Unconscious competence: you know how to do a task well

Everyone strives for the level of unconscious competence, but getting there is a process, and when working with team you need to be able to recognize what level of competence each individual has. In summary, Mr. Petcovic notes that we can think about the principles of mastery, autonomy, and purpose that make for better performance and satisfaction at work. We can learn to recognize levels of competency in individuals. With some intuition of their personality type (think Myers-Briggs standards) you can foster growth in your team individualistically and in this way become a master at developing your people.

SciPhD highlighted great ways to learn how to work alongside your team, but how can you leverage the power of a larger community with some of the same skills? This is what the SciPhD workshop calls building your network. An important question to ask yourself is: why should I network? Some answers to this question include, you want to be the first to find a job or get your resume to hiring managers. Furthermore, you may want to keep in touch with anyone who may be of value to you and provide a pipeline for opportunities in the future. Networking is crucial to professional success. How you  network successfully, especially in the COVID era, can sometimes be a difficult task.

Building a network is all about making connections that will somehow allow you to stand out, get your resume on the hiring managers desk, and be made aware of opportunities available to you.

SchiPhD suggested to build your network by categorizing contacts into groups including people in your current job position, people in your previous job position, social contacts, and alumnae networks. However, the people you put on these lists should be returning your calls, replying to your emails, or taking you up on an invitation to lunch. It is important that you know you are building a genuine network that you can leverage. SciPhD co-founder Randy Ribaudo Ph.D. made sure to add that no one in this network you build is meant to offer you a job, but they know someone who will! Building a network is all about making connections that will somehow allow you to stand out, get your resume on the hiring managers desk, and be made aware of opportunities available to you. Importantly, building a network and achieving success means you can also provide similar resources to people within your network.

SciPhD brought students together through the concept of networking all the time. You’ve probably heard about this already – developing an elevator pitch. An elevator pitch has three components: what you do, how you do it, and how you engage with others. Keeping these factors in mind, Dr. Ribaudo notes that we also need to remember our audience and deliver the correct pitch to the correct individual, as well as shift into the learner role as described above. You might be asking, what if you can’t prepare your elevator pitch ahead of time? In this case, it is helpful to ask general questions of the person you are networking with – things like their current role, general career advice, or their own experiences that you can glean from your interactions with them. It was recommended to always carry business cards with you to distribute if need be, and to collect them from individuals you network with. Immediately after networking, SciPhD suggests that you write down brief details and unique bits of information from each individual, so that you can reach out to them with some tailored messages later. With these tips in mind, your networking will be much more fruitful and effective!

Stay tuned for part 3 in the 2021 SciPhD Leadership and Business Skills series!

This article was edited by Junior Editor Rukia Henry and Senior Editor Samantha Avina