My Journey from Academia to Industry: Lessons Learned the Hard Way. Part 1
I’ve had an awesome career. From growing up in a small town in Alaska, to getting my MS and PhD in astrobiology where I studied astrobiology and geomicrobiology, to now being the CTO of Cemvita, where I lead the development and deployment of nature-inspired biosolutions to enable the transition to a carbon-neutral future. Through this meandering career journey, I’ve gotten the privilege to appear in AWS YouTube Videos with Orange Theory Fitness, help launch a global Energy Data Platform at AWS, pioneer genomics in the energy industry, and slay data silos building a global data engineering and architecture team at Wood Mackenzie. In all these career adventures, I keep thinking that if I could see where I’ve ended up my grad school self would be amazed.
These pivots haven’t come easy. In fact, the initial transition from being a scientist in academia to my first industry role as a geologist at Hess was a huge step change. I didn’t come from a family of white-collar office workers, so I learned many of these lessons the hard way, and I’m proud of my deep-seated tenacity turning missteps into learning opportunities that have galvanized my career.
As we continue to ramp up technology scaling and development to actively tackle the energy transition, there is a glaring need for scientists and interested academics to tackle some of industry’s toughest challenges. While everyone may not have as steep a learning curve as I did, there are definitely a few key themes and lessons that have emerged in the 15+ years I’ve been at this. In the spirit of helping and enabling the next generation of scientists transitioning to industry, I present my list of the most impactful lessons I’ve learned. These are broken down into four main themes, and I’ll present these as four sequential posts. My opinions are my own, all names have been changed to protect the innocent, and as with everything in your life, your mileage may vary.
Build on!
-Liz
Theme 1: The transition from academia to industry will affect who you are at your very scientific core and sense of identity.
Lesson 0: Accept that this is going to be hard , and going to require hard work
If changing the carbon intensity of fuels was easy, it would have been done. If scaling technology was easy, then every promising technology or invention you read a press release about would be part of our tool kit. We’d have full battery recycling with no environmental impact, we’d be powered here by a decentralized microgrid where we recycle our water and waste, and we’d also have blockchain and crypto-style distributed ledger technology ensuring that the carbon we save is tracked and verified. The reality is it’s really, really, really hard, but I don’t believe impossible, and if you’re reading this, I hope you don’t think it’s impossible either.
The momentum of our current energy ecosystem favors hydrocarbons. That said, moving from n=1 experiments to full scale transformation is a fight that’s worth fighting. It also means that in that tech scale up process, things get messy, and things can sometimes be confusing if you’re not used to it. There is a learning curve for things like how the VC model works, how techno-economic assessments are put together, and how companies prioritize limited resources to ensure that lab experiments enable the broader movement towards innovation.
Understanding all of these pieces takes time and exposure, and you need to be prepared for these differences between academic research and commercial research focused on turning the results in your lab into a product. The good news is, all the skills you learned in grad school to manage that work still apply in industry.
Lesson 1: In industry, it’s about the destination, not the journey.
As a company, we have a fiduciary obligation to our board, to our investors, and to ourselves to deliver results. We need to be pushing the envelope, doing incredible science as quickly as possible, and using resources responsibly. In academia, it’s much more about the knowledge you amass along the way.
For me, time is my biggest limited resource: there are only 24 hours in a day, and I know that for everything I spend time on I’m making a tradeoff to not do something else. It also means that efficiency and getting to the end destination as quickly as possible is what I optimize for.
If it takes me an hour to write a letter of recommendation, I might do a great job, and feel a sense of accomplishment, but if I can have ChatGPT do that same task in 3 minutes, it’s my obligation to leverage ChatGPT (full disclaimer that I’m well versed in the limitations and risks with ChatGPT, please spend those extra 57 minutes not writing that letter of rec to research the do’s and don’ts of using off-the-shelf AI tools).
Likewise, if you’re stuck on a coding problem or how to do the statistical analysis for your results, don’t spend hours and hours googling it, seek outside help. You don’t get extra credit for reasoning it through yourself and taking longer. In fact, that’s bad.
If you need help with experimental design, ask for it. If you need help optimizing your workload to get more done in less time, raise your hand. We optimize for results, not how long or how much effort it took you to get there.
Lesson 2: The Technical can be easy, the Interpersonal can be tricky
Personal anecdote time: I struggled the first time I was a leader of leaders. I’d been a front line manager before, and it was great. When I became a director leading multiple teams I encountered a puzzling disconnect.
Up until that point, I’d been a lead by example person. If you were doing something, I’d show you exactly what I wanted done. My superpower was hard work and hustle, and I would outperform anyone. Which, as I write that, is a pretty nice way of saying I had no chill. As mentioned, I like to optimize for efficiency, which has somehow become a core personality trait. If I have a to-do list or things on my mind, I’m in the office at 6:30 am. I dig deep and get sh*t done. For the first years of my career, that became my methodology, and I quickly got used to being the hardest working and smartest person in the room, with a quirky personality and insanely high standards.
I’ve also spent portions of my life falling into the fallacy where I expect everyone else to act just like me. This is a polite way of saying I had to learn empathy a little bit later in life. I wasn’t good at reading people, wasn’t good at understanding why people reacted poorly when the inputs seemed logical, and would just sit there in confusion when human systems didn’t respond to the logical models I’d made in my head.
When I was working as a director, I’d be in the office around 7 am every morning, pull together my to-do list, and just hammer things out. One of the team members who reported to me, this amazing scientist who will remain nameless, would come in at 9:30 and work until 6:30 or 7 pm. That overlap was good since there were many junior team members and we were staffed 12+ hours a day.
However, as soon as this scientist would come in every morning, before even putting her stuff down or getting coffee, I’d corner her as she walked in, with new items and priorities to add to her to-do list.
I did this for about a week before she made a point of saying “Hi Liz, how are you? Good Morning. Would you like some coffee?” At first I was confused. I didn’t want coffee, I’d been here for a few hours already and I had new things to do (again, remember, no chill). Upon reflection I realized it was a polite way of telling me to calm down, give her a minute, and also ask about her. Turns out we’re not all mindless lab drones who just want to talk about work and how to optimize for the 99.9999th percentile of efficiency.
This may seem simplistic now, especially because the thing I’m proudest of is the relationships I’ve built and teams I’ve had the privilege of creating. This interaction was the first real turning point in a paradigm shift that led me to realize that everyone has a different style, and engaging with those styles was actually an advantage, not a weakness.
Lesson 3: What got you here, won’t get you there
“What got you here won’t get you there.” If you’ve never heard of this before, it’s a really good book. It’s also a saying that lives rent-free in my head as I think about what it takes to pivot or advance your career.
Granted, it depends on what your definition of “there” is and where you’re trying to go. For me, my north star is having an impactful career driving the energy transition. For others it could be doing experiments that transform how we understand the front lines of knowledge, or even be a valued contributor in a larger organization.
The key thing here is that what enabled you to be a great grad student, TA or RA are, as a general rule, NOT what you need to be successful in industry.
Why is this true? Academia prepares you for a few narrow things, mainly to thrive in the confines of academia.
The evolutionary stresses in grad school aren’t the same as those in industry. The skills and techniques that got you to graduation or your first (or second) job, likely aren’t the same as those required to move forward and onward.
It’s also true on a more macro level in academia too. The skills that lead to a successful PhD aren’t necessarily the same as a successful postdoc or someone who gets tenure. Careers have different parameters that you need to optimize for along their journey, and the difference can be even more stark between academia and industry.
Why? A few reasons: Applied science can be a dirty word. Taking shortcuts to get to the answer quicker can be seen as bad. Academia can be a zero-sum game as you compete for grants and scarce resources. You are frequently pushing the envelope of how we understand the world in a very narrow way, not thinking about how your piece is integrated into a rapidly-moving and scaling system.
There is a learning curve to thriving in industry, the sooner you recognize and take stock of that, the better off you’ll be
So…what do you do about it?
As we wrap up theme one, I want to throw out one tool that has a huge impact on how I prioritize my most limited resource (time): the Eisenhower matrix. Especially early in my career I’d get overwhelmed by everything I have to do, which would result in panic, I’d start to lose executive function, and just shut down and not know where to start.
Much like how my dorm room would be really clean when I had an assignment to do in college. When I get overwhelmed, I go back to the things that I can control and feel good about. Clean dorm rooms aren’t going to improve biosolutions to solve pressing energy challenges, so it’s about how you get past that.
This is the technique that resonates for me.
I write down EVERYTHING I have to do. Things like ‘train on my track bike’, ‘pick up my dog from daycare’, and ‘make dinner’, then I write out an Eisenhower matrix with a column for “important” vs. “not-important” and “urgent’ vs. “not urgent” and put each in the corresponding bucket.
Now, here’s the tricky part.
Things that are urgent, but not important, like cooking dinner, I’ll delegate, and pick up take out or get delivery.
Things that are important and not urgent, like training for the track on my bike, I’ll decide, and choose the option that best fits my schedule, such as going to a spin class instead of driving all the way to the track.
Things that are both important and urgent, like picking up my dog, I do, with enthusiasm
Yes, I may eat more takeout and delivery than other people, but it frees up my time and enables me to ruthlessly prioritize what I need to do and drive how I spend my time.
