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.

 

What do you think you got out of The Data Incubator?

There are two very valuable things that I got from The Data Incubator:
(1) Exposure (not expertise!): I learned the latest data science buzzwords, and got some brief experience using the tools. It was enough to allow me to speak intelligently during my interviews without having glaring gaps in my knowledge. I had a high-level understanding of the tools necessary to solve different types of data science problems, and through the problem sets, I gained confidence that I could learn the low-level implementations when necessary.
(2) “Personal” introductions: My participation in TDI got my resume to the top of the stack. Having their seal of approval led me to not only have more conversations with employers, but to have ultimately more fruitful conversations with employers.

 

What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?

Don’t be intimidated. As a biologist, I was in the minority amongst math and physics PhDs in my TDI session. I had never taken linear algebra (gasp!), and I’d only been programming seriously for about a year. I had to work harder than most of my cohort to solve the problem sets. However, I solved the problems in whatever way I was best able at the time, asked embarrassingly obvious questions, and I learned by trying.

More importantly, I uncovered the value that my “softer-skilled” background brought to interviews. I gained valuable experience explaining complex data science problems to non-technical audiences during my time as a clinical researcher, and I think it may be part of the reason why I had great success during my job search.

What’s your favorite thing you learned while at The Data Incubator? This can be a technology, concept, or whatever you want!

I learned to value the unique skills of others. Especially in a field as technical as data science, one might expect there to be a right and wrong way to solve a problem. Instead, I learned that data science is a bit of an art — there are a variety of ways to tackle a problem, each with their own caveats and advantages. It was a lot of fun to hear the clever ways that my classmates navigated the challenges of each problem set, and to discuss which was subjectively the best solution given the constraints of the problem.  In some cases, I think we cumulatively arrived at better solutions than the answers that we were being graded against. This lesson has held true at my job over this past year — the more clever minds brainstorming together, the better the outcome. It’s also a lot more fun.

Where are you working? And tell us a little about your new job!

I’m a data scientist in the Data Science & Informatics department of a large agricultural company — DuPont Pioneer. I’m currently focused on understanding and improving models for predicting field nitrogen content for growers. I work remotely from my home office in Pittsburgh, and I love what I do. I never envisioned myself working towards big-data solutions for farmers, mostly because I’d never considered how many issues they face that can be improved by data science endeavours . I’m glad that Pioneer persuaded me to think twice about it!  The data science of agronomy is picking up pace, and there are so many complex, interesting problems left to solve. Though I love what I do, the best thing about my company is absolutely the people — they have universally shown themselves to be kind, clever, and generous with their time. I feel valued, and I’m thankful for finding such a great fit.

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