Alex Siega – Data Scientist

From Russian Language Analyst to Data Scientist

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3.) What obstacles have you had to overcome in your career/college journey?

When you come from a non-traditional background, people doubt your ability to contribute to their field. All I can say is: don't let them get to you! When I was making my transition from human to programming languages, I met (many) people along the way who didn't want to give me the opportunity to prove myself, citing my lack of experience and formal education in computer science. It was tempting to revert back to the safe space of language analysis, where I knew I could achieve. How could I thrive in an unfamiliar discipline and culture? However, I saw a need for someone who understood both sides of tool development, and wanted to be the person to fill it. This conviction carried me through meetings with any and all relevant people to help me in my quest; finally, after months of searching, I found a team that believed in the need, too.

As the person in the room not like the other, it can be a tiring, frustrating crusade to advocate for yourself, especially when you're seeking to change the status-quo. However, through my experiences, I've learned that when you take complete ownership of your passion, you're capable of nothing short of revolution!

1.) What is your chosen STEM field?

Data science

2.) What made you decide to choose this field?

My path to data science wasn't particularly straightforward! After studying Russian at Middlebury College, I served in government as a multidisciplined language analyst for three years. In that role, I was immersed in data; specifically, my work involved distilling multiple streams of language into one cohesive narrative. To achieve this, my workflow consisted of multiple web-based tools that made seemingly simple tasks last hours (even days!) longer than necessary. Frustrated, I began to reach out to tool creators to provide feedback on current features, as well as provide insight that many were lacking into how analysis was conducted on the ground. My role in bridging the analyst and developer populations eventually evolved into embedding myself directly into development teams, and it was there that I realized that I actually desired to make tools rather than solely partake in the feedback loop.

Thus, I moved away from D.C. to study coding at the Flatiron School in New York City. When I was looking for positions after graduating from the Flatiron School, I was attracted to data science because of the diverse competencies expected of professionals in the field. One must be proficient at statistics, coding, and analysis to uncover relationships in the data, but also be able to effectively communicate findings. Being a data scientist means that I can answer new questions, overcome new challenges, and develop new, diverse skills every day -- even if I'm working with the same dataset. It was a perfect synthesis of my prior experience in analysis and my current coding skills!

 

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