Changing to an industry job
without leaving your Ph.D. behind

Arushi Prakash

Towards the end of my Ph.D. in Chemical Engineering, I made the decision to transition into the field of data science. The months following the decision can only be described as a marathon in self-education, networking, and deliberate practice. The marathon paid off when I got a job as data scientist at Zulily, an e-commerce company, with a great team of data scientists who themselves transitioned from different degrees.

At Zulily, I build machine-learning models to make shopping more engaging for customers. This domain is quite different from my graduate research, where I developed physics-based simulations of molecules. In fact, the data science group at Zulily contains Ph.D. graduates from Mathematics, Biomedical Engineering, Aquatics and Fisheries, and Petroleum Engineering, who work on projects that are fairly different from their graduate theses. As I witness the journeys of my colleagues and me in the field of data science, I want to discuss changing careers while retaining the best parts of your Ph.D. training. [Disclaimer:  Some examples might be more relevant to people transitioning to tech jobs, but the larger learnings will be relevant to everyone]

            In academia, my graduate research was in basic sciences, my research projects were mostly self-led, and my advisors checked in with me regularly but were hands-off. In contrast, in the industry, my work has immediate impact on the company’s business and product. Moreover, my projects are designed and led by technical and product managers, and check-ins happen once or multiple times a day. Essentially, when compared to academia, my industry job has greater collaboration with both technical and non-technical stakeholders, ownership of smaller components of projects, accelerated timelines, well-defined deadlines, visible impact, and greater project management support.

            Given these clear differences, how can someone with years of academic training survive in an industrial setting? One way might be to disregard our previous experience and rebuild from scratch. However, it is difficult to throw away work habits developed from years of training, and many of those skills are indeed very valuable. Therefore, I recommend identifying lessons from academia that can directly translate to success in industry:

  • Self-sufficiency – Having designed and successfully completed research projects, we are accustomed to being self-reliant in all aspects of the project. As such, we can deliver on projects without constant nudges by managers; they can look to us for reliability.
  • Perseverance – We are comfortable with failing projects, revising manuscripts under harsh comments by reviewer #3, and persevering in the face of many odds to complete projects. While other people in the industry might get frustrated by failures, we are comfortable with going back to the drawing board and iterate multiple times to deliver the best solution.
  • Identifying which problems to solveWe can think about problems in the long term, and also understand which parts of the problem can be solved in a shorter term with the resources at hand. This comes in handy when designing projects for success. Case in point, for your Ph.D. thesis, you were able to identify the open problems in your field. Some problems were difficult to tackle, so you zoomed into a subset that you could solve with the tools at your disposal within the timeframe of a graduate degree.
  • Higher threshold for cognitive dissonance – We are extremely comfortable when faced with a completely new idea or perspective. We can quickly absorb material, especially research papers, to understand new concepts and reason about them, which makes our learning curves shorter.

In the same vein, there are aspects of academic training that do not prepare us completely for the challenges in the industry:

  • Communication – As academics, we are comfortable engaging with experts in our field. For the most part, we do not engage with the broader, non-technical audience. In fact, it was recently shown that people associate scientists with poor communication skills. In contrast, in the industry, we are constantly communicating with non-technical stakeholders like product managers, executives, and designers. It pays off to improve communication skills, even by seemingly easy exercises like explaining your thesis to your grandmother.
  • Collaboration – We are accustomed to owning research projects solely, or with a few collaborators at most. However, in the industry, responsibility and credit is generally shared with a much larger team. Here, being open about your work and maintaining good communication between collaborators is essential.

People transition out of academia for various reasons. Once we switch into our new jobs, we might feel that we have lost the career capital, brand, or portfolio that we had already built. These thoughts are disempowering. However, if you can identify the lessons from academia that allow you to do well in your current job, it can act as a success multiplier.

Dr. Arushi Prakash is a data scientist at Zulily

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