Data are becoming the new raw material of business
The Economist

Data-Driven Solutions for Agronomy: Alumni Spotlight on Lindsay Bellani

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. Lindsay was a Fellow in our Summer 2015 cohort who landed a job with one of our hiring partners, DuPont Pioneer.

Tell us about your background. How did it set you up to be a great data scientist? 

I love biology — in particular, neuroscience — and I had every intention of pursuing a career in academia. I received my BS in biology from UNC Chapel Hill, and went on to study neurogenetics at The Rockefeller University in New York City. I decided to pursue a bit of a non-traditional PhD project — I wanted to understand why mosquitoes bite some people more often than others. Though I didn’t know it at the time, it was this choice that led me to a career in data science. I began by setting up a clinical study wherein we recruited hundreds of volunteers and tested them for attractiveness to mosquitoes. We then collected a bunch of different samples from each of them — everything from blood to questionnaire results. We wanted to understand which, if any, of these factors were predictive of mosquito attractiveness. At the end of the study, I was left with a whole lot of data and not a clue what to do with it. With the help of our University’s biostatistics department, in particular Joel Correa da Rosa, I learned how to use machine learning to do predictive modeling. It was a difficult, real-world dataset, and its analysis led to many interesting debates as to what was the best way to handle its various nuances. I began coding on my own to try new ideas, and eventually Joel and I became equal thought partners in the process. I actually ended up working out of the biostatistics office instead of my own lab for a few months before my thesis defense. Through this process, I began to love the art of data science, and I was encouraged to hear from others that I had a knack for it. It was all of the rigor and analytical-thinking and puzzle-solving that I loved about bench science, but even better. Seeing my enthusiasm and aptitude, my husband recommended that I apply for The Data Incubator. I kind of applied on a whim — I think I filled out the application the same day it was due.

I’m grateful for the path that led me to a career in data science. My background in biology has given me the ability to think scientifically about a problem — to understand the nuance of data collection, and how to design a good experiment, and which analyses might provide the biggest insights. Because I ran a clinical study and none of the members of my lab had a background in machine learning, I had to practice explaining this complex data science problem to non-technical audiences, which has been an asset when presenting results to the business side of the company I work for. It’s been a very natural transition, which I think speaks to what a good fit it is for my personality and talents.
From a research perspective, working in a vibrant academic setting also meant learning how to ask bold questions, even at the risk of sounding stupid in front of a large group of mentors and peers–something I’ve done more than I care to admit. For me, finding the right question to ask is just as important as having the technical expertise to find an answer, and that’s one of the things that makes Data Science so exciting.

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