The unpredictable path

As graduate students and postdocs, the ebb and flow of your path will likely be shaped by two critical factors: the skills you develop as you hone your craft in academia, and the network you build along the way. The former, sometimes grouped under “transferable skills,” can take you to some unexpected places.

It’s late fall 2018. I’m a new postdoc, packed with my colleagues onto a small research flight bound for the coastal ecosystems of British Columbia. Our goal: to understand how tiny microorganisms transform carbon as it moves from forest to ocean, and how these microscopic changes ripple out to affect our global climate. (That project eventually became Levy-Booth et al.,ISME 2019).

On the ancient Grumman G-21 Goose ferrying us to our field station, one student confides that she can’t picture herself continuing in academia. Over the roar of the World War II-era engines as we bank through the fjords, she asks how she could possibly pivot toward industry or business when all she knows is working in a lab.

I told her what I’ll tell you now: she had spent the last four-plus years developing deep problem-solving instincts and approaches to hard questions. These are skills that would serve her anywhere. That student is now CEO of a microbial genetics platform company, so I feel pretty good about that advice.

Skills to Pay the Bills

Since that flight, my own career has taken turns I never anticipated. I once imagined a linear route to a faculty post: teaching, mentoring, publishing, tenure. Instead, the path was weirder, and, ultimately, far more rewarding.

After the COVID-19 pandemic upended nearly everything, I stepped off the faculty interview circuit to manage a sequencing core, and later took a leap with a young synthetic biology startup that desperately needed help rebuilding its sequencing pipelines. Today, I lead an engineering team at this company, racing to design the next generation of genetic therapies. I still roll up my sleeves with sequencing from time to time, but most of my energy now goes into pushing the bleeding edge of AI and machine learning.

Reflecting on that journey, I can trace much of my success back to the essential habits and skills that I developed in grad school and rely on every day.

1. Solving really hard problems

Research trains you to live with uncertainty. You learn to form hypotheses, test them rigorously, and stay curious when the data refuse to behave. In industry, those same instincts are invaluable. Complex, high-dimensional problems, whether in biology, engineering or AI, reward people who can think experimentally, adapt quickly and stay comfortable in ambiguity. The best scientists I’ve worked with are not just technically sharp. They are relentless in refining how they approach the unknown.

2. Writing and communication

If there’s one underrated superpower academia gives you, it’s the ability to communicate with precision. Graduate school forces you to think clearly because you have to write clearly, whether that’s explaining your data in a paper, defending a proposal, or distilling years of research into a ten-minute conference talk.

In industry, clear writing is clear thinking. It is one of the skills I spend the most time coaching my senior leaders to master. The ability to distill a multi-faceted problem into a short memo or crisp presentation often determines whether a project moves forward or stalls. The polish and discipline you develop drafting papers and defending proposals directly translates to business outcomes: better alignment, faster decisions, fewer misunderstandings and ultimately, more revenue.

If you’ve spent years explaining your work to skeptical reviewers or mixed audiences, you’ve already mastered one of the most valuable skills any team can have: the ability to make ideas understandable, persuasive and actionable.

3. Programming and data wrangling

Whether you’re parsing genomes, optimizing experiments or modeling AI systems, fluency with data is your leverage. Learning to program at any level teaches you to automate the repetitive and focus on the creative. I still rely on those skills daily: not just to write code, but to think computationally about systems, experiments and decisions. Even a little bit of data literacy can dramatically expand your impact in both research and industry.

Even more critically,understanding that technology increases your leverage against hard problems changes how you solve them, and the value you bring to your organization.

4. Bonus: people and project management

This may be more aspirational. Not every graduate student has a chance to really lead their projects. But picking up the less sexy parts of research—grant writing, budgeting and supervising undergrads — are valuable experiences that will jump-start a transition to industry. After all, managing a research project isn’t that different from leading a startup team. Both require juggling competing priorities, mentoring junior colleagues and keeping an eye on the big picture while troubleshooting the day-to-day. Academia doesn’t always emphasize it, but learning to communicate clearly, give constructive feedback and organize complex workstreams are superpowers that scale well beyond the lab.

Finding your own path

Looking back, it’s clear that the core skills I rely on every day didn’t come from any single course, title or job description. They came from the habits forged in graduate school: learning to wrestle with uncertainty, to think and write with clarity, to wrangle messy data into meaning and to keep projects (and people) moving in the right direction.

Is this perspective universal? Probably not. But having mentored several students through their own transitions into industry, I’ve seen first-hand how these same skills, and the ability to use them to raise the bar, can make a career not just successful, but delightfully weird and unexpectedly rewarding, too.