Data are becoming the new raw material of business
The Economist

From Eco-Friendly Batteries to Random Forests: Alumni Spotlight on Matt Lawder

At The Data Incubator we run a free eight-week Data Science Fellowship Program 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. Matt was a Fellow in our Winter 2016 cohort who landed a job with one of our hiring partners, 1010data.


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

I defended my PhD dissertation at Washington University in St. Louis, a few weeks before coming to The Data Incubator. I was part of the MAPLE lab in Energy, Environmental, and Chemical Engineering (I know, it’s a mouthful). Our lab focused on physics-based electrochemical modeling, mostly geared toward Li-ion batteries.

For my main dissertation project, I studied how batteries age under different real-world cycling patterns. Most cycle life estimates for a battery are based on simple constant charge and constant discharge patterns, but lots of applications (such those experienced by batteries in electric vehicles or coupled to the electric grid) do not have simple cycling patterns. This variation effects the life of the battery.

Both through model simulation and long-term experiments, I had to analyze battery characteristics over thousands of cycles and pick out important features. This type of analysis along with programming computational models that were used to create these data sets helped give me a background to tackle data science problems.

Additionally, I think that working on my PhD projects allowed me to gain experience in solving unstructured problems, where the solution (and sometime even the problem/need) are not well defined. these type of problems are very common, especially once you get outside of academia.


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

More than I can fit in a couple of paragraphs! The most important thing for me was learning all the functionality of different programming languages and packages. Coming from a background where I had programmed in Maple, VB, and a little bit of Matlab and SQL, learning Python (and all of its different packages), Spark, etc. opened up so many possibilities for doing new types of analysis. Knowing these tools, greatly sped up my ability to conquer new problems.

Completing miniprojects on each subject was instrumental in feeling confident about applying the techniques we learned in real-world situations. It was definitely a pressure packed environment trying to complete everything on time, but it forced you to know each subject inside and out. Looking back on the program it’s amazing to look at the amount of code you have produced.

Beyond the subject matter, working together with so many other driven people was a great experience. And the network of employers that were brought through the program for happy hours and panel discussions always helped showcase all the different ways data science is being used in industry. I made my first connection with 1010data (where I will be starting a job at the end of the month) at one of the happy hours. So I think those were pretty valuable!

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