What’s your ideal working environment: a lab, a surgery operating room, or your home office? Actually, there’s no need to choose just one. Play your cards right and your career could give you the opportunity to do a little bit of everything.
It’s hard to sum up the career path of Xander Twombly, Data Scientist and Analyst at The Motley Fool. He started with a Physics major, ventured into biophysics and computer science, and did a stint at NASA (involving virtual reality, robots, and medical imaging, to name just a few) before settling into his current role.
We talk with Xander about his colorful background and his thoughts on what it’s really like to work in research.
What is your academic background?
I received a BA in Physics from Reed College in ’87, then went to Johns Hopkins to work on a PhD in Physics. Within a semester I realized that my future was destined to be either a mediocre theoretician or an experimental post-doc at the age of 40, hoping that my name would appear with the 50 other authors of a particle physics paper. The appeal was just not there, so I started taking some classes in neurophysiology in the biophysics department.
Brains were a fascinating study in systems theory, so I jumped ship and joined the biophysics department in my second year at Hopkins. Spent a couple years there getting the requisite chemistry and biology background, but my greatest interest was in the rapidly growing field of neural network computation.
I caught a talk from one of the system neuroscience professors at the medical school that was doing research on awake behaving primates, and studying the nuts and bolts of actual biological neural networks, and managed to talk my way into his lab to model them with computational neural networks.
I spent the next 6.5 years developing methods to model spatial data received through the system of touch (think of the “image” of a Braille character you can “see” with your finger), and how this image data was processed from the peripheral nerves at the fingertips into the primary cortex.
Given the decade I spent, the number of fields I had the opportunity to study was huge, and I credit that to a lot of the wandering and success of my later work history. Not only did I have the opportunity to get a thorough backing in most physical sciences, but was also able to build in mathematics (pattern recognition, statistical modeling, etc.), computer science, some medical courses (even including a bit of surgery). Admittedly, this required a very patient advisor (not to mention spouse!), but the benefits were huge for the time spent. All told, it was about a decade of graduate school.
What did you do right after you graduated? Did you have a break between undergrad and graduate studies?
I had originally planned on working immediately after Reed, but then got cold feet in late January and scrambled to find a graduate program whose application deadline hadn’t passed. Hopkins was the only one, so I applied. Not the most inspiring story, I know.
What are you currently doing?
For the past five years I’ve worked as kind of a hybrid data scientist and analyst for a company called The Motley Fool, which is an investment advice company that is oriented towards hosting a huge online investment community as well as providing specific investment advice.
Largely the role is that of a computer scientist, with projects in data mining “community intelligence” or the wisdom of crowds to look for above market investment returns, providing analytic services to our members to help construct long-term portfolio and investment management strategies, investigating the behavioral aspects of finance, and trying to build models of investor response (in particular, trying to help people to make certain types of moves that have classically cost them large amounts of the returns they could achieve from investing), and some rather free-wheeling research into modeling what specific individuals like/dislike about stocks and how to suggest companies that they might be interested in looking into (think of the Spotify or Netflix recommendation systems).
What types of tasks did you perform in a typical day?
What exactly do I do each day? I ask myself that sometimes, and have a difficult time answering. Lots of reading in areas of interest. (This is generally applicable to my job, but not necessarily so—we have a company policy that strongly encourages keeping a broad field of interests as a way to keep us both interested in the world around us, and open to new ideas that come from non-traditional places. Very Reed-like).
A good deal of programming gets done, both for operational systems and building research systems to test out our financial theories. I spend a reasonable amount of time on our member boards reading and responding to posts, and write the occasional white paper.
Exactly when all this gets done is a bit of a blur, because I work from home, and tend to keep odd hours. I probably work 60+ hours a week because I love what I do, and when inspiration strikes in the middle of the night I still wander out to my office to flesh out the ideas.
What are some of the challenges you face in your current position?
The only dislike that I would say is that telecommuting is not for everyone, and you need to have a very particular mindset to do it. My company is on the East Coast, so I go out to visit people maybe three to four times a year, but otherwise the daily contact is through electronic means, and that is really lacking in the daily camaraderie of an office.
You’ve worked in a range of roles as a scientist. Can you give a brief explanation of what they were and how they compare to each other?
Before The Motley Fool, I was primarily employed at the NASA Ames Research Center in Mountain View. Many projects, many different roles, but basically all of it involved me working as a fundamental researcher in different areas of computer modeling (somewhat ironic since everything I learned about computer science was self-taught—amazing what humming a few bars and faking will do for you).
The projects were:
Medical Image Analysis: early development of surface and boundary reconstructions of biological tissues from CT, MRI, and ultrasound images. This was used to develop an early telemedicine application for sharing detailed image reconstructions before PC graphics became so powerful that you could render everything on your desktop.
Virtual Reality: Created generalized shared virtual environments that 3D objects would live in, and that multiple people at different remote locations could simultaneously interact with. Pretty much the same as a multi-player video game these days, but also included some augmented reality work.
Robotic Control: Worked on developing a neural-based balance control mechanism for a “Scorpion” robot, a six-legged critter built by one of the other researchers.
Medical Image Fusion: Developed methods of melding high-resolution images of critical body systems (heart, lungs) that could be taken on earth with lower resolution ultrasound images that could potentially be taken in space. The idea was to try and build a “digital astronaut” that could be used to help diagnose problems on extended/distant missions (Mars?)
Wiring Faults in Aging Aircraft: Worked with a team that was developing methods for searching for minute insulation breaks in the miles of wiring in aging aircraft systems. Lots of signal analysis stuff.
Some of these projects I was simply a hired gun, working as a member of the team. Other times I was supervising other researchers or programmers, developing the grant proposals, and spending way too much time fighting for funding rather than actually doing any work.
Basic research is like that, unfortunately. As you progress into really wanting to do your own research, the competition for dollars at the government level is fierce, and you often end up spending enormous amounts of time just trying to get/defend funding, end up working on something that you are only tangentially interested in because that is what someone wants to fund, or both.
I started doing some consulting work just to pursue my personal interests, and developed a number of smaller software projects for people on the side.
One project that really caught my interest was the CAPS financial picking service that came up in Beta with The Fool, and after floating around on their boards and promulgating the usual loud and opinionated internet take on how calculations were being done, I was invited to put up or shut up and provide some technical assistance.
Our relationship developed pretty quickly from there, and I bailed on fundamental research about a year later and hit the commercial work world. Couldn’t be happier.
What advice would you give to current college students who are science majors?
Generalize. If you end up in an academic or competitively funded research environment, the ability to be competent across fields will serve you well because even specialized skill sets need to be cognizant of how you can present your skills and ideas, either via teaching or papers or grant proposals, and often that will require you to tie your work to a broader context to make it relevant.
For those who end up in business, the most interesting places that I know of to work are not looking for narrowly focused individuals, but generalists who are capable of absorbing whatever is necessary for a particular task, applying it, and rinsing and repeating.
Few employers will expect you to be more than competent with the tools of the trade—what they will expect is for you to have a flexible mind and set of interests, and hopefully a unique and creative approach to the types of work your do.
Embrace the scrappy entrepreneurial approach, be absolutely tenacious in what you do, but also be aware of who your customers are so that if they are not interested in what you have to offer, you can pivot to something else that may be common ground (Hey, you can’t just be interested in one type of thing in life, can you?).
Homework time! Xander talks about the importance of following different interests and being able to apply an entrepreneurial mindset to your career. Not sure how to do this? Check out our post on how you can apply the principles of Divergent to your job search and career.