Students on the Blog: Interning as a Data Scientist
by Yujie Sun (MS Statistics ’19)
Finding an appropriate internship opportunity is hard for students. During my journey in finding an internship, many resources at the school were useful. Tools for Clear Speech helped me improve my English, and the GCMC Advisors helped me modify my resume and urged me to take steps that would help me get where I want to go. Attending the Graduate Internship Course Networking Sessions also helped me learn about internship opportunities from other students. Eventually, I got my internship opportunity by applying on Zicklin CareerLink.
The company where I interned, Tal Solutions, is a FinTech company that provides insights to financial-services clients. My job is as a Data Scientist, not as an Investment Analyst. Therefore, it’s possible to be a Data Scientist in the finance industry even though you do not have a background in finance.
My daily responsibilities are to train and tune machine learning models, analyze data provided by clients, and much more. There are also business needs, such as doing research papers and looking into algorithms that I need to build.
Whenever I complete analysis tasks, I always have to think where the analysis will go. My supervisor looks into the data and understands the financial aspect behind it. From that perspective, becoming a Data Scientist does not require some industry knowledge, however understanding the stories behind the data always requires more industry-related knowledge. This inspires me that maybe I can develop more industry-related knowledge when I enter a particular one.
Ultimately, being a Data Scientist at a Fintech company is challenging since you need to research questions you may face and try to resolve. From another perspective, this will also practice your research ability.
This internship was a terrific opportunity for me and I really recommend an experience like it for students.
Finding an appropriate internship opportunity is hard for students. During my journey in finding an internship, many resources at the school were useful. Tools for Clear Speech helped me improve my English, and the GCMC Advisors helped me modify my resume and urged me to take steps that would help me get where I want to go. Attending the Graduate Internship Course Networking Sessions also helped me learn about internship opportunities from other students. Eventually, I got my internship opportunity by applying on Zicklin CareerLink.
The company where I interned, Tal Solutions, is a FinTech company that provides insights to financial-services clients. My job is as a Data Scientist, not as an Investment Analyst. Therefore, it’s possible to be a Data Scientist in the finance industry even though you do not have a background in finance.
My daily responsibilities are to train and tune machine learning models, analyze data provided by clients, and much more. There are also business needs, such as doing research papers and looking into algorithms that I need to build.
Whenever I complete analysis tasks, I always have to think where the analysis will go. My supervisor looks into the data and understands the financial aspect behind it. From that perspective, becoming a Data Scientist does not require some industry knowledge, however understanding the stories behind the data always requires more industry-related knowledge. This inspires me that maybe I can develop more industry-related knowledge when I enter a particular one.
Ultimately, being a Data Scientist at a Fintech company is challenging since you need to research questions you may face and try to resolve. From another perspective, this will also practice your research ability.
This internship was a terrific opportunity for me and I really recommend an experience like it for students.
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How to Become a Data Scientist