Career transition in my 50s

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In 2019, I was almost 50 years old. Everything looked wonderful at that time. I was a staff scientist in NIH (equivalent to research scientist in extramural institutes) and played a role of a Co-PI to supervise daily research work in a melanoma biology laboratory. Up to that point, I have been working in the same laboratory for almost 15 years, starting as a postdoc and then later being promoted to staff scientist. Over the years I generated mouse models and reagents that are widely used in the melanoma research field. My PI has a good reputation in both research and mentoring. He encouraged me to explore new fields and develop connections, so I have lots of friends in the research community. Life was good.

One day my PI uttered that he was not a “lifer”, so he planned to retire in a couple of years. At first I thought he was just complaining about the recent stress. However, as he brought up the idea to more people, I decided to check with him and got a solid answer that he intended to retire in a few years so our laboratory would be closed. I was in shock, because I never considered the possibility that I might need to find another job someday, although I had to admit that it was extremely naive and wishful thinking. 

I was very grateful that my PI revealed his plan way ahead of the schedule, leaving all the lab members sufficient time to prepare for the alternative. I first discussed with him if I can stay to lead preclinical studies for all the PIs, but got a very straightforward no: “contract company can do that.” Another idea is that, after he retires, our branch would likely hire a new PI to fill the position, and he could negotiate for me with our branch to work for that person. Since this would be a junior PI, I would spend most of the time on bench work. I had a complicated feeling about this supposedly merciful opportunity. I had effectively played a supervisory role in this lab for many years, accumulating knowledge and experience not only in research, but also in administration, mentoring, and collaboration. I don’t want to waste these efforts and intellectual assets. I kept devising new ideas with the aim to stay in my current branch, but they got rejected one by one by either my PI or other PIs. I found a very big dilemma: my knowledge and experiences were supposed to advance my research career, but there is no career path for staff scientists in a research institute. Every PI just wanted to hire people to generate more data from bench work. 

In the following two years, the pandemic held everything on and made job hunting extremely difficult. I got a few interviews from small biotech companies, and none of them worked out. The reason behind it is worth another full-scale article. In the first year people worked from home most of the time. As I was still supervising, I tried several approaches to maintain the research works in our laboratory. Since we have accumulated a large amount of data in the previous years, I think it would be a good idea to collaborate with PIs of the newly established Cancer Data Science Laboratory (CDSL). To my very surprise, I got very enthusiastic responses from them. They are interested in a diverse spectrum of types of data, developing new methods to analyze them, and actually looked for collaboration with experimental biologists all the time. The mouse data were generated by well-controlled studies, including genetics, age, sex, or even pathology. They can serve many purposes: bench-marking, validation, inter-specific studies, etc. I soon got involved in several projects on tumor evolution.

As these collaborations were progressing, I learned more about biomedical data science. Many experimental biologists considered data analysis as the activities of running pipelines to generate plots for visualization. However, this is only the very small technical part of data analysis. Working with my collaborators, I had to learn about the mechanism of each analysis technology and the features of the data it generated, as well as its capacity and limitations. I also needed to discuss with them what kind of analysis can reach the aim of this project, and if the result can be used to test the biological hypothesis. Although I never received professional training in computational biology, in my whole career I am always interested in the mathematical models of evolutionary biology, ecology, and population genetics. I routinely read papers in these fields and tried to interpret the concept of these studies. These learning experiences helped me to communicate with my collaborators and understand their ideas. Interestingly, my collaborators began to ask me to join the meetings of their other collaborative projects, because I could serve as a “translator” between experimental and computational biologists.

In the second year of pandemic, I was still struggling in finding my next step if my PI retired. Observing my interest in data science, my PI suggested that I should take some data analysis courses, converting myself to become a computational biologist. While appreciating the suggestion, I know that it did not work in that way. I could learn how to run some pipelines in commercial packages, doing some data curation works. However, for many biologists of the younger generation, such skills are already included in their job requirement. When I asked if I could join the bioinformatic team in our branch, I got rejected again. The reason was like what I expected: no need to waste a precious position to a person with only entry-level data analysis skills. Meanwhile, I got a few more interviews from industry here and there, and again none of them worked out. The clock was ticking.

After pandemic finished, I got involved in more and more collaborative projects with PIs in CDSL. I was asked to work out many different kinds of questions: the right type of data to test a specific method, the biological context of an analysis result, the type of technology for a specific biological question, and most importantly, how to extract information from the results of analyses, and how to interpret it. I became very busy from these works, as I still full-time managed research works in my current lab. Meanwhile, my PI had finalized his schedule of retirement. I needed to find out my next step as soon as possible.

Up to that point, I had been co-authors in eight papers from the collaborations with computational biologists, and planned to submit a manuscript as a co-corresponding author with a biomathematician. When I compared these studies with my own mouse works, I found that they were all for cancer modeling but by different approaches. I can learn from both sides by putting them in the same biological context. Figuring it out, I became confident that I could contribute to computational cancer research. I attended group meetings in CDSL to discuss projects routinely, so I could give my suggestions or help to solve some small problems in their projects from the perspective of an experimental biologist.

Finally my PI announced officially the date of his retirement, so I could move forward with my job seeking plan. I submitted my request for joining CDSL and got an enthusiastically positive response. CDSL made great efforts to create my position, and I received a very warm welcome from PIs there. It was a very special moment in my career: so many professionals and experts helped me to learn about a new research field, allowing me to complete this transition. In this position I aim to bridge experimental and computational biology. Here are a few goals I set for my new job:

  1. Make mouse data standard bench-marking datasets for testing computational methods.
  2. Build collaboration between experimental and computational biologists, focusing on data and method sharing.
  3. Build workflow for interpreting data in the biological context.
  4. Incorporate biological relevance into the development of data analysis methods.
  5. Generate data on demand of PIs in CDSL.

This is the dream job for me- I like to say I will "breed (real) mouse with (computer) mouse". For those who made my transition happen, thank you for your teaching and support. For those who had said no to me, thank you for pushing me back to the track toward this wonderful opportunity.

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