alumni
Eye on Alumni: Aimee Barciauskas '16
Aimee Barciauskas (Master's in Data Science '16) is a data engineer at Development Seed in Washington, DC. She has found several ways to use her knowledge of engineering, data science and machine learning for social good.
Your first position after graduating from the BSE Data Science Master’s was at Nava, a public benefit corporation that creates software to improve how the government serves its citizens. Was this the path you had expected to take when you decided to do the Master’s?
I knew regardless of the title, I wanted to be working at a company with a mission focused on social good. While I was hoping to find a position as a data or machine learning engineer, the mission of a company and who worked there were more important to me. I didn’t find many opportunities that offered both a strong social mission and a position as a data or machine learning engineer.
Working at Nava wasn’t the path I expected to take when I decided to do the master's - but when I decided to to do the master's it was an exploration itself. I knew I wanted to do more with data - but I wanted to explore what that meant. Did I want to be a data scientist? A statistician? A data journalist? And one thing I realized while I was at the Barcelona School of Economics was that I really did like engineering and I was pretty ok at it. So once I graduated, I was pretty sure engineering was still the right path for me.
In your new position at Development Seed, you’ll be working on a Cumulus project to create an enterprise cloud tool for NASA’s Earth Observation data. How will you be helping to make this massive data set more accessible and impactful?
Cumulus is moving NASA’s satellite earth observation data to the cloud. This data is currently stored in NASA’s distributed active archive center’s (DAACs). Moving this data to the cloud is going to improve the accessibility of the data to both NASA scientists and the public, with increased reliability and transparency.
What did the Master’s give you that helped you take these next steps on your career path?
At Nava, I contributed towards a number of data-driven projects. For example, I performed natural language analysis and text mining of the google group supporting the Web API we built. Another example is a blog post I wrote on comparing state-level rates of poverty with Medicaid enrollment. The master's gave me the confidence and tools to work on these projects which left me more fulfilled in my contributions to Nava projects. It also gave me the confidence to apply to Development Seed, where I have already been able to apply some of the things I have learned about machine learning in my work with the engineers there.
You’re also a chapter leader of DataKind DC. How long have you been involved with that organization and why is it important to you?
I have been working with DataKind since August 2016 as both a volunteer and data ambassador. DataKind mirrors my personal goals and ambitions - I was so excited to discover them during a DataDive back in Seattle of 2016. DataKind is a way to contribute passions for engineering, data science and machine learning towards social good regardless of where I work or what I do day to day. DataKind provides this outlet for so many passionate people. Innovation will only happen is if people voluntarily do more than what is asked of them. I’m thrilled to be a part of a community that is as excited about both cool technology and positive social change as I am. It’s a thrill because of the work and because of the people I meet.
There are more and more graduate programs for Data Science at universities all over the world, and some of them you can even do entirely online. Why did you choose to come to Barcelona for this program? Was there anything about it that stood out from other programs you considered?
I chose BSE because I was impressed by the faculty and the program of courses. It was important to me the program emphasized mathematical theory. It’s relatively easy to teach yourself data visualization, coding and to apply different machine learning algorithms - so I knew it was important to focus on the “hard stuff” while in school. For me this would be the mathematical theory and proofs behind why the models and methods work. Also graduate school in Europe is more affordable than in the states.
As an American, how did you find life in Barcelona? Would you recommend to other US graduate students to study in Barcelona?
The quality of life in Barcelona is hard to beat. The food, weather and beauty of the city were breathtaking. And it’s affordable. However, grad school is a lot of work so I would tell other US graduate students to make sure to create extra time before or after school to really enjoy the city. I was too busy with school work to enjoy Barcelona. I’m still planning to come back to Barcelona to really enjoy it as a vacation.