How I became a data scientist

Although I am a planner by nature, I never planned to be a data scientist or work in the tech industry.

I started in a Ph.D. program for sociology at University of Washington in 2008 with the intention of becoming a professor. By 2011, I'd had enough of academia. Although I had a great research assistantship and was doing well, the future job prospects and pay for a professor didn't seem worth toiling away for several years (not to mention enduring the dreary skies of Seattle). 

Luckily, I had taken 7 quarters of statistics and a couple data management classes -- skills that come in handy beyond dissertations. 

Because I had been hedonistically working in Yosemite every summer to pursue my rock climbing goals, San Francisco - a weekend's trip away - sounded better than DC or NYC. I emailed everyone I knew who was even remotely connected to the Bay Area (friends and acquaintances I'd met in Yosemite, college and high school acquaintances who'd ended up there, friends of friends...) and asked them to put me in touch with ANYONE who had ANYTHING to do with data in their professional lives. This worked. I started driving from Yosemite to San Francisco every other week with a handful of coffee dates scheduled over one or two days. I was fortunate that one of my climbing partners generously let me sleep on her couch in Berkeley every time I came through town. 

I used my new personal connections and job websites. I applied to research organizations, nonprofits, schools, and anything I thought my academic background was good for. An acquaintance of mine encouraged me to apply for a data analyst position at the tech startup where she worked. As someone who was still carrying around a flip phone, I hadn't considered the tech industry. She invited me to lunch with the lead data scientist, whom I immediately liked. He was smart, motivated, fast-paced, easy to talk to. I applied and did the take-home data test, which was a whirlwind of Google searches but I ultimately did well. I then had an onsite interview where I completely failed the coding section, but did well enough in the rest of the interview that I was hired anyway!!! Bless their souls for taking a chance on someone who didn't know SQL or Python and only knew the basics of R.

I learned SQL and R on the job within a month. After about 6 months, my supervisor generously paid for the 60-hour, 10-week General Assembly night class for Data Science to gain more statistical methods useful to a career in tech. 

All in all, I started my job search in October 2012 while still working in Yosemite. I moved to San Francisco by March 1, 2013. I learned SQL and R by April 2013. I'm still picking up Python and other skills along the way. 

And even beginners can give talks at conferences and write PR articles (The Next Web and BetaNews)! I've been asked to speak at Data 2.0 Summit 2013, Data Week 2013Import.io Data Summit 2014, and a SF Data Mining Meetup in 2015 (at 12:35). While I'm not a natural public speaker, it's great practice and I always say yes if someone asks. 

It turns out the tech sector is a great, dynamic place to launch a career because companies are often looking for smart people who can fill a variety of roles and perform a variety of tasks. You'll learn the latest technological tools for your job, meet highly motivated people, and most likely make a bigger salary than other sectors. I know several people who have started in the tech sector and moved into education or nonprofits (which they feel was their calling) and do very well thinking outside of the box in those sectors because of the things they learned working in tech.