It’s a long way to the top (Mount Baker, September 2017, Credit: Devin Bishop)
I’ve been on a lot of interviews over the past 16 months, like a lot. More than a dozen. One of the questions that always comes up is some variant of:
“Tell me about yourself”
“What’s your background?”
“How did you become interested in data science?”
So I’m going to use this space to talk about my data science journey. I’m only partly doing this under duress (thanks General Assembly — more on that later!).
I think of my career path so far as surprisingly linear — at least compared to many of the other data scientists I know.
When I was in college, I thought I was going to be a math and government major. I took a lot of the core math courses, like linear algebra and multivariable calculus, but eventually decided that track wasn’t for me. I ended up studying government and economics, and added a bunch more math, like statistics and econometrics.
I spent most of my time at Cornell involved in politics, serving in multiple leadership roles for the Cornell Democrats. I wanted to work in politics. So when I graduated it was predictable when I moved to Washington, DC and got a job at a political consulting firm that specialized in public opinion research. Hey, would you look at that, I found a way to blend politics and numbers.
In that first job, I was able to do everything in the survey research process. Data processing, questionnaire design, analysis, report writing, client presentations, you name it. I got to work for some awesome clients and manage important projects, like two months of nightly tracking surveys in Ohio in September and October 2012 for the Obama campaign.
In my second job, I stopped thinking about data as only survey research and I expanded my horizons. I was working on an Analytics team and straddling the line between primary data collection (survey research) and secondary analysis on research conducted by others. I was working with, and learning from, data scientists and got practical knowledge of using regression and classification tools to answer real-world problems and help clients make better decisions — like deciding which states to buy ad time in and to send field staff to, which voters to appeal to, and what to say to those voters.
I learned more than I realized while I was there. But it unexpectedly came to an end after the 2016 election. I spent most of 2017 outside: hiking, camping, climbing Mount Baker, and looking for a new job in politics where I could expand my data science skills. To no avail. It was an incredibly frustrating year but it also came with some amazing views.
All of my previous experience was tied up in closed-source tools, like Stata, and proprietary client research. I needed to build a portfolio and develop a professional network outside of politics. I wanted to expand my data science toolbox and develop stronger foundational understanding for the tools I was already using. And that’s how I ended up at General Assembly this January.
Now, I’m about 1 week away from the end of the Data Science Immersive program. Going through the program, I realized how much I learned on the job, but now I have much stronger foundations than I did before. I built more confidence talking about my work and why it matters. (That was one thing that was drilled into me while I was in politics: the things that matter most to the client are how the statistical results impact their business. The results need to be meaningful, valid, and the client needs to understand how to apply it to their business).
General Assembly validated a lot of things I pretty sure of beforehand. I enjoy working on data science problems. I have a deep analytic toolbox to draw from and I’m ready for the next opportunity to build my data science skills and apply them to a diverse set of challenges.
You can check out my other blog posts here. Thanks for reading!