- With whom and where did you do your internship?
I worked closely with the Pricing and Structuring team in the Baltimore headquarters of Constellation, an Exelon company. Constellation is an energy company providing power, natural gas, renewable energy, and energy management products and services.
- How would you explain what you did to the non-technical person?
I did a mixture of data analysis and data cleaning. My first assigned project was to discern patterns out of noisy data, while my second was to retrieve and prepare data sets listing energy usage in different areas of the country to help Constellation update existing price data.
- What was as you expected about your internship?
My internship was very quantitative, which is what I expected coming into this role.
- What was unexpected about it?
I learned an incredible amount, much more than I anticipated. While I had some good background, it was invaluable to learn to apply it as a part of a team in a fast-moving environment.
- What methods did you use? and what software?
For the data analysis, we used Python, and performed the analysis with a mixture of linear regressions, neural networks, and some classification methods. For the data retrieval and input, I used Python to prepare the data and MATLAB to input the data.
- What did you learn that you didn’t get from the classroom?
I learned that not every problem has a set solution, like it does in the classroom. There’s some exploration that will need to be done on certain problems where you’re unsure of how to move forward. In that sense, it’s just like research problems. However, due to time constraints it’s impossible to go as in-depth as one would like–Constellation has deadlines it needs to meet, so when you get a “good enough” solution, it’s best to leave it at that unless it’s explicitly decided that more time should be spent on it.
- What advice would you give future interns?
Don’t be too hard on yourself if you can’t figure out the solution to an assigned problem as fast as you would like–it’s an internship, and it’s a time for you to be learning.
- What are your careers interests and goals in the near term?
I would enjoy any data-science based career, but I have a particular interest in finance applications. I would love to be able to perform research on forecasting models and on new methods coming up in data science that could be applied to industry.
- What do you enjoy doing outside of work?
I make some time to enjoy playing the piano, mountain biking, and reading, among other things.