Andrew is a Quantitative Analyst for Data-Driven Yale. Before joining the group, he worked for the Institute for Market Transformation in Washington, DC, where he modeled city-level environmental policies and worked with real estate companies to reduce energy consumption within their portfolios. He graduated from American University’s School of International Service in 2012.

Welcome to Data-Driven Yale! Can you tell us about your new role, and what type of projects you’ll be working on? What are your goals for these projects?

Great to be here! I’m going to be working on a variety of things for DDY, but I will mainly be doing data analytics and visualizations and managing anything that is a larger-scale programming-centric task.

As far as projects go, the first thing I’m going to be working on is putting together a web portal for the Urban Environmental and Social Inclusion Index. My goal for the Index is to make something that is very accessible. My background, before I started programming, is in international relations. I had some technical knowledge, but I was not a programmer, not a scientist. This has helped me really understand the struggles that policymakers have when they are trying to access large amounts of data. It can be difficult, so I want to make tools that are usable not only by researchers but also by policymakers.

I’m also going to be involved in the Global Climate Action project, which is all about examining how cities, states, businesses, companies, and investors are making climate commitments. I will probably be involved on every project at least a little bit.

Have you always been interested in sustainability and the environment?

Yes—in high school, I was very interested in physics. The first physics topic that really interested me was nuclear energy. I loved that stuff. When I was 10 years old, I did a school project on Hiroshima and Nagasaki. Ever since then, I have been fascinated by nuclear energy. Taking physics in college and actually seeing how it worked was amazing. As I went through college, my passion for the environment persisted. From nuclear energy, I shifted to renewables. Once I started studying renewable energy, as someone who is data-driven, I realized, “Why would we generate more energy when we can reduce our energy consumption?” I realized that so many of our carbon emissions come from using energy within our buildings, so that’s how I walked the path from being a physics student interested in the environment to being someone that really wanted to work on energy efficiency policy. I just followed the numbers the whole way.

After college, I worked at a small institute in Washington, D.C., called the Institute for Market Transformation (IMT). It’s a non-profit whose mission is to improve the energy efficiency of buildings in the United States. I was first exposed to data science and programming through IMT’s need to step up our analytics to the next level. As I worked more and more with data, I realized that I really enjoyed it. That was when I decided to do NYC Data Science Academy, as a way to really shift my focus from policy to a more data science, analytics-focused role. It was three months, 9 to 5, like a full-time job, or as students can relate, a full course load. It was really cool to finally be exposed in a real way to data science topics.

What are your thoughts on how well (or not) environmental policy takes advantage of quantitative analysis and big data right now?

I think we’re getting better. The explosion in easier-to-use programming tools in the last five years or so has really helped bridge the gap between the data people and the policy people. That being said, there is still a real need for people that have a solid understanding of numbers that can help craft policy. When there is a lot of a data, people can often be paralyzed by their inability to understand the numbers. Often, people should just be pushing forward when they know broadly that they’re moving in the right direction, but they don’t because their analysis isn’t perfect. One of the things that IMT was really good at was realizing, “As long as we know the ballpark of where we’re going, then we know we can push towards doing something right.”

WSED

I love this quote from your LinkedIn: “I am a data scientist who excels at communicating complex ideas to non-technical groups.” How do you feel like this skill will help you excel at DDY and with environmental work in general?

These issues are so complex. One of the drawbacks of learning cutting-edge data analytics is that, you have to learn so much that people often forget what it feels like to know nothing. One of the things that has really helped me in the past is that, I’ve always been close enough to knowing nothing that I have always been able to get in the mindset. I never want to forget that—what it’s like to be at square one. If you can put yourself in the mind of someone who is afraid of math, then you can start to explain things in a way that makes sense to them.

A big inspiration for me in that sense is my old boss at IMT. He had a real estate background, and then got involved with IMT, an environmental think tank. He was incredible at speaking to people from other industries, especially the real estate industry, and putting things in a way that they could understand. I think that many of these cutting-edge groups struggle in the sense that everybody is too brilliant. At Data-Driven Yale, I’m hoping that I can be the village idiot, in the best way possible.

What advice would you give to students interested in the intersection between data science and other social/political issues?

My advice is a bit of a twist on what you typically hear. People often say, “if you want to get involved in something, just go try it, get involved. See if you like it. Try learning just the basics of programming. Ask someone if you can contribute to their projects.”

Having tried to make that transition into data science after college though, my advice to people is to first get comfortable feeling like you don’t know anything. Get comfortable feeling that you don’t know enough. That’s how I’ve made progress. I have often felt overmatched and like I have no idea what I’m doing. When I went into data science, I knew that I would feel like an idiot for a year or two. But, I also knew that was okay, because everyone feels like an idiot at first. So, my advice to other students is, jump in and be excited to not know anything. That just means that you have room to grow and learn.

 

 

 

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