Data are becoming the new raw material of business
The Economist

Making (LinkedIn) Connections: Alumni Spotlight on Xia Hong

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. Xia was a Fellow in our Summer 2015 cohort who landed a job at LinkedIn.

 

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

I am an experimental physicist in soft condensed matter by training in my PhD program at Emory University. There are three things that I think have helped me a lot to become a good data scientist:
1). The solid background in physics and math that I obtained back in my college. The knowledge itself isn’t necessarily reflected in my day to day work now. However, the training of logical thinking and critical thinking is really beneficial in a long run.
2). Persistence in finding root causes. The massive amount of data can easily leave you feeling swamped. I believe that always asking why until you get to the true cause of the problem is really essential. Sometimes, the insights are hidden behind and need our motivation to dig them out. No matter if it’s driven by natural stubbornness or original curiosity, I find the persistence usually a great help for walking the last mile to the final discovery.
3). Passion for solving problems using data. There is a joint program in our department where I took computer science courses for a masters degree. In the course projects, I started to find my passion in solving practical problems using data science approaches. Now I am working on product analytics and I cannot imagine how tough it could be without that passion and curiosity about what we can do to improve it.

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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 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? Matt Lawder

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. 

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Leveraging a Physics Background to Achieve Data Science Success: Alumni Spotlight on Andrew Yue

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. Andrew was a Fellow in our Fall 2015 cohort who landed a job with one of our hiring partners in Washington, DC.


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

Andrew YueI’m an experimental nuclear physicist by training. I had the great privilege to perform research at the National Institute of Standards and Technology (NIST) for nine years. NIST is a Department of Commerce laboratory that specializes in the science of measurement (metrology) and its application to industry. My research focused on precision measurement techniques with neutrons to advance our understanding of fundamental physics and to improve industry services offered by my group.

There are two things that I think have helped me get to where I am:

1) Like most physicists, I think I have a natural propensity to tinker with things well outside my expertise. Taken too far, this can be a bad thing. But, applied appropriately, it’s exactly the kind of attitude needed to learn and keep up with the ever-changing field of data science.

2) Having focused on precision measurements in my research, I’ve seen time and time again how much the environment in which I performed my experiments impacted the data and informed my analysis. The parallel to data science is that my training has taught me that a deep understanding of the problem and how the data was collected are what allow you to ask the right questions and produce meaningful results.  Continue reading

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Alumni Spotlight on Matthew Lamont: Tips and Tricks for Data Incubator Success

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. Matthew was a Fellow in our Fall 2015 cohort who landed a job with one of our hiring partners, AdTheorent.

 

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

I was a computational high-energy nuclear physics experimentalist, working on the RHIC facility at Brookhaven National Lab on Long Island. This involved data mining on multi-PB of data to extract small signals on the order of hundreds of GB using distributed computing. This involved cleaning the data and developing algorithms to extract these signals. Once analyzed, these results were written up and published in peer reviewed journals.

 

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

I was very happy with the Data Incubator course. I was introduced to many concepts that I had just touched on in some online courses I had taken prior to the course. By having weekly mini projects, I was forced to understand the material more. Whilst there were lectures to accompany the mini-projects, in order to complete them I was forced to do some research on my own which was a valuable thing. We weren’t just fed the answers.  Continue reading

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Transitioning from Physics Research to Data Science: Alumni Spotlight on Chris Chabalko

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. Chris was a Fellow in our Fall 2015 cohort who landed a job with one of our hiring partners, Sotera.

 

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

I’ve always been interested in science and examining how things work. Before I could talk, I disassembled part of a washing machine. In that particular instance my victory was short lived; I was told to return the appliance to its original condition, which I promptly did.

My curiosity formed the basis for a successful research career. My technical background includes analysis in several different areas of physics, but data analysis remains a common thread. As one example, my dissertation involved analyzing structural and aerodynamic data from a transonic aerodynamic system. I excelled at the data analysis aspects of the work in a way that a traditional aerodynamicist probably wouldn’t have. I was able to identify correlations between key mechanisms in different systems and develop visualizations to easily express the conclusions.

Two aspects of the work I enjoyed the most are: the determination of root causes and the satisfaction of presenting these in simple terms. These traits align well with data science.  Continue reading

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Alumni Spotlight: Phillip Schafer Talks Transitioning to Industry and Advice for Applicants

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. Phillip was a Fellow in our spring cohort who landed a job with one of our hiring partners, Optoro.

 

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

Phillip Baker SchaferI got started doing computational physics as an undergrad at Yale. I had a summer job in the Geology Department doing numerical convection simulations and found that I really liked writing code to solve a problem. After college, I taught high school physics for a year and then went back to the Yale Geology Department to work on data analysis for a precipitation study. Back then my toolset was limited (Excel, Matlab, a bit of C) but I got to cut my teeth on finding interesting patterns in data.

When I started grad school at Penn State, I thought I wanted to be a more straight-ahead experimental physicist. I worked for a while in an atomic physics lab, but found that I missed writing code. I looked around the physics department and found a group that was doing computational neuroscience. My advisor, Dezhe Jin, had an idea for a project using ideas from neuroscience to design better speech recognition systems. I enjoyed building up a new way of attacking an old problem, more or less disregarding the standard methods used in the field. I also got to try my hand at a lot of machine learning and statistical methods in the process.

As I was finishing my PhD, I was looking around for new and interesting problems to work on. An email about The Data Incubator circulated around the physics department and I thought I’d give it a try. I knew I’d made the right decision because I had a lot of fun doing the take-home problems in the application. I had to apply twice before I was accepted, but my persistence paid off.  Continue reading

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From Particle Physics to Data Science: Alumni Spotlight on Andrew Leister

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. Andrew was a Fellow in our summer cohort who landed a job with one of our hiring partners, Freddie Mac, after finishing his PhD at Yale. Here’s his story:

 

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

Before applying to the Data Incubator, I received my Ph.D. in particle physics. I spent years analyzing proton collisions from a particle detector which collects several petabytes of data each year. This experience set me up to be a great data scientist for two important reasons. For one thing, it gave me plenty of experience working with real data. From this, I developed a good understanding of each stage of the data analysis process and the challenges associated with it. The other reason my background prepared me for a career in data science was that it helped me develop great skills as an experimentalist in general. I learned not only what to look for when designing an experiment, but also how to set up and conduct the experiment to obtain meaningful results.  Continue reading

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From Mathematics to Freddie Mac: Alumni Spotlight on Daniel File

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. Dan was a Fellow in our summer cohort who landed a job with one of our hiring partners, Freddie Mac. Here’s his story:

 

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

I have Ph.D. in pure mathematics, and my dissertation was in number theory. After graduating I was a visiting assistant professor at a large university and later at a liberal arts college. Doing mathematics taught me to think deeply about hard problems, but I was lacking some of the skills I would need to be a data scientist. To prepare for The Data Incubator and a career as a data scientist, I looked for opportunities to improve my statistics and coding skills. For example I helped my department develop a statistics curriculum using R, and I supervised undergraduate research in number theory using C and Python.

 

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

The Data Incubator is incredibly good at recruiting highly motivated people who are very talented to be Fellows, and then introducing those Fellows to employers who want to hire a data scientist. This happens through Partner Panels, Happy Hours, Pitch Nights, and the growing network of alumni. I learned a great deal during the program, and much of that happened by interacting with the other Fellows. I also got a lot of great feedback from the other Fellows about my capstone project. The Data Incubator gave me support for job placement by helping me write a great resume, giving me advice about contacting employers, and doing interview preparation. Continue reading

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Using Data Science for the Government: Alumni Spotlight on David Krisiloff

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. David was a Fellow in our winter cohort who landed a job with one of our hiring partners, Sotera, after finishing his PhD at Princeton. Here’s his story:

 

David KrisiloffTell us about your background. How did this set you up to be a great data scientist?

I did my PhD in computational chemistry, which, as the name suggests, focuses on computer simulations of chemicals. The difficulty in computational chemistry doesn’t come from a lack of knowledge of the underlying physics. Rather, the difficulty comes from computational complexity – 100 year computer simulations aren’t an effective way to obtain a PhD. Computational chemistry does a great job of training researchers to think about how their computer simulations really work, how to approximate the really hard parts, and when those approximations do (and don’t) work. Those are all really important skills for a data scientist confronted with a large data set distributed across multiple computers (aka “Big Data”).

 

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

My fellow cohorts and I loved to discuss this during the fellowship and we settled on three important aspects of the Incubator. First, The Data Incubator gives you a nice overview of the current techniques in data science. That includes everything from what’s a random forest to the latest technologies for distributed computing. Second, The Data Incubator exposes you to how data science is actually used by companies. By hearing from many different companies you get a feeling for the different types of problems that they tackle and how data science offers real solutions in a variety of fields. Finally, The Data Incubator helps expand your network both by introducing you to companies looking for data scientists, and also (but just as importantly!)  by introducing you to the other Fellows. Continue reading

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Columbia Astrophysics Postdoc Moves to Capital One Labs: Alumni Spotlight on Brian Farris

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. Brian was a Fellow in our spring cohort who landed a job with one of our hiring partners, Capital One, after completing his postdoc at Columbia and NYU. Here’s his story:

Brian FarrisTell us about your background. How did this set you up to be a great data scientist?

My background is in computational astrophysics and numerical relativity. I did my Ph.D. work at the University of Illinois at Urbana Champaign, then went on to a postdoc at Columbia and NYU. For my research I was performing large scale simulations of accretion disks around black holes. These simulations are performed on supercomputers and generate very large data sets. In working with this data, I developed specific skills which carry over directly to data science. However, I think the experience I gained in approaching problems scientifically, thinking critically, and using data to communicate a story clearly was the most valuable.

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

The most valuable thing I got out of The Data Incubator was the ability to meet people from industry through the partner panels and happy hours. For me this served two purposes. First, it was an extremely efficient way to do a lot of networking in a short amount of time, which greatly increases the chance of finding a job. It is much easier to initiate a dialogue with a hiring partner if you have already met someone from the company in person. Continue reading

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