At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Alex was a Fellow in our Fall 2015 cohort in Washington, DC who landed a job with our hiring partner, NAUTO, in Palo Alto, California.
Tell us about your background. How did it set you up to be a great data scientist?
I went in a straight line for 28 years, and then zig-zagged all over the place. I pursued and received a PhD in Math from UCLA, which culminated two decades of focusing on math. However, during my grad studies I developed other interests, and following grad school I did a lot of political activism and founded a not-for-profit bicycle shop. After that I worked in K-12 Education for 3.5 years, first at Green Dot Public Schools, then at McGraw Hill Education. That gave me a lot of business experience that has proved to be useful connecting the technical side of data science with the business side.
What do you think you got out of The Data Incubator?
It helped me get from the stage of unconscious incompetence – not knowing what you don’t know about data science – to conscious incompetence – knowing what you don’t know, and knowing how to fix that. After five hard weeks of homework, you have some pretty good skills, but more importantly, you have a good idea of where you need to spend time learning, and how to learn. If I was an employer, I would feel comfortable hiring people who have been through The Data Incubator, since they are (a) accomplished hard workers and (b) have shown a willingness and ability to learn a very new field.
What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?
I would investigate what the course covers, and try to start working on the curriculum very early, especially building their mastery of Python. The curriculum proceeds at a breakneck pace and every bit of exposure a fellow can have beforehand is helpful. I thought the pace was, honestly, too fast, so you will want to get ahead of it.
What is your favorite thing you learned at The Data Incubator?
Scala and Spark work like hand in glove, it was phenomenal to see how much smoother that workflow can be than a set of technologies that were not really built to work together as much, such as Python and Hadoop MapReduce.
Could you tell us about your Data Incubator Capstone project?
I created an app that distills traffic collision data for California. It really does three things. First, it provides summary analysis of collisions for any city in California. Second, it classifies cities by their traffic safety profile, and suggests traffic safety improvements based on that classification. And third, it makes the case that insurance companies could save more than $1 billion annually by lobbying at the city level for road improvements.
And lastly, tell us about your new job!
I worked in a dual role, first and foremost as a data scientist leading NAUTO’s risk scoring efforts. And second, in sales, building partnerships and acting as a technical liaison with leading insurers, insurance service providers, and insurance consultants. As a data scientist, I:
- Scoped, conceptualized, and designed four versions of Nauto’s risk estimation algorithm, VERA.
- Coordinated the efforts of seven staff across Android, backend, frontend, data science, and design teams to implement VERA’s data collection, implementation, and UI/UX.
- Designed, trained off bootstrapped data, and implemented engineering to deploy VERA scoring models in production. Principal tools: Python, sci-kit learn (sklearn), Docker.
I recently moved into a new role as Head of Driver Behavior, Data Science where, as tech lead and manager, I direct the Driver Behavior Team, which develops algorithms to characterize driver behavior and risk based on NAUTO sensors and other team’s algorithms. We also support NAUTO’s insurance partners, and act as tech leads for that area.