Alumni Spotlight: Isaac Carruthers – From Neuroscience to Data Science

We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring Data Scientists. Isaac was a Fellow in our Fall 2014 cohort who landed a job with one of our hiring partners, Quantitative Brokers, after receiving his PhD in Physics from the University of Pennsylvania.

Isaac, you have a pretty diverse academic background. Can you tell us a bit about that?

My first real academic interest was in programming. I started in 9th grade, and quickly got hooked on the flexibility and power; the sudden fine-grained control over what had previously been a mysterious machine. I’ve been programming ever since: academically, as well as a hobby. In college I majored in physics and computer science, going on to a physics PhD program after graduation. Once in graduate school, I somehow found myself doing research in computational neuroscience, working on models of populations of neurons in the auditory cortex.

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

It was immensely helpful to talk openly with the different employers, to build a sense of what different companies value, and a sense of how to adjust my own diction and mannerisms to signal an appreciation of what they value.

I think that the most valuable thing that I got from the Data Incubator was a real immersion in industry culture. It was immensely helpful to talk openly with the different employers, to build a sense of what different companies value, and a sense of how to adjust my own diction and mannerisms to signal an appreciation of what they value. I feel that just being in frequent contact with the Data Incubator’s Hiring Partners really helped me know how to present myself when interviewing, and additionally helped me build a much better sense of what sort of company I wanted to work for.

Almost as valuable as the cultural immersion were the mock-interview sessions. Tech interviews remind me of the SAT: you can greatly improve your score by building a familiarity with what sort of questions tend to show up. Having frequent practice in answering technical questions, time-pressure and all, really helped keep me in the right mindset to perform well in interviews.

What elements of your Computational Neuroscience training do you think have been useful for your current work as a data scientist?

Computational Neuroscience is, at just about every level, all about finding the signal in the noise. It’s about taking large amounts of very abstruse data, such as tiny fluctuations in electrical fields at different places in a brain, and trying to find a way to relate that data to what the animal as a whole is experiencing. In some ways this is more technically difficult than a lot of data science work: there’s very little room to use intuition or common sense; it’s very hard to come up with an idea of what a particular neuron “should” be doing. Of course, intuition and common sense are skills that need to be practiced, but I’d say that my background has given me a solid grounding in the technical aspects of data analysis that are most broadly applicable to different fields.

Can you tell us about your project at the Incubator?

When I was looking for a project to get started with, I basically went out looking for datasets that I found interesting. I ended up stumbling across the historical data for Lending Club’s peer-to-peer loan program. The dataset immediately appealed to me for the straightforward and actionable opportunities that it offered. Lending Club loans are assigned interest rates based largely on the FICO score of the applicants, but there are many more details available to potential lenders, such as each applicant’s housing status, state of residence, and income. I set out to build a model that could use this information to pick out high-interest loans that were not likely to fail.

In the end about half the work on my project went towards getting all the data into a usable format, and the other half into building the model itself, validating the model, and doing control analyses to make sure that the model was actually as useful as it seemed to be. By the end of the program I had a fairly convincing product that could pick out good loans at a rate well above chance, and I had a set of analyses that gave a good estimate of just how much money the product was worth. [Isaac was too shy to admit this, but his returns were 15% higher than those in the dataset.]

Thanks for your time, Isaac! In closing, what advice would you give to someone who is applying for The Data Incubator?

The one thing I will say is that you should be prepared to take on a mindset different from what you may be used to in academia.

If you’re still in the process of applying, I would say to make sure you review your computer-science, probability theory, and math. You’ll be competing against a lot of quite talented people with strong quantitative backgrounds, and having a good grounding in quantitative problem-solving will be important for passing the interviews.

Once you’re in, I don’t know that you’ll need much in the way of advice. It’s a good program, and if you show up, stay engaged, and focus on your own weak-points then you’ll get a lot out of it. The one thing I will say is that you should be prepared to take on a mindset different from what you may be used to in academia. This may not be true for all people, but in academia it’s easy to settle into the mindset of “what problems sound interesting?” (interesting either to you, or to your advisor’s grant committee) whereas in industry it seems that the much more important position is often “what will be useful?” You may be asked to take a much more goal-oriented approach than you are used to, and you may have to put more thought into specific problems that you are trying to solve, or practical metrics that you are trying to improve.

Related Blog Posts

Moving From Mechanical Engineering to Data Science

Moving From Mechanical Engineering to Data Science

Mechanical engineering and data science may appear vastly different on the surface. Mechanical engineers create physical machines, while data scientists deal with abstract concepts like algorithms and machine learning. Nonetheless, transitioning from mechanical engineering to data science is a feasible path, as explained in this blog.

Read More »
Data Engineering Project

What Does a Data Engineering Project Look Like?

It’s time to talk about the different data engineering projects you might work on as you enter the exciting world of data. You can add these projects to your portfolio and show the best ones to future employers. Remember, the world’s most successful engineers all started where you are now.

Read More »
open ai

AI Prompt Examples for Data Scientists to Use in 2023

Artificial intelligence (AI) isn’t going to steal your data scientist job! Instead, AI tools like ChatGPT can automate some of the more mundane tasks in your future career, saving you time and energy. To make life easier, here are some data science prompts to get you started.

Read More »