How I used a bootcamp to get a data job in finance

For those who want to work as a data scientist at a financial services firm, there are many career opportunities, and there are various paths to success that you can take. Getting an internship or summer analyst position is a key, while doing a data-science bootcamp will also bolster your skill set.

During my sophomore year at Georgetown studying mathematics and finance, I won a national Accenture consulting case competition and got an internship in their financial services group in New York.

I had always been drawn to the hedge fund industry, and – by conveying to on-campus recruiters that I was a good fit and my desire to work in investment management – I landed a summer analyst position at Millennium Management the following year.

The asset management industry has been becoming more quantitative and data science has been a very hot trend within it. To enhance my resume and programming skills, I enrolled in an NYC Data Science Academy Bootcamp. Over the course of three months, I developed raw technical skills and familiarized myself with applicable data-science methods that connected theory I had learned in school to real-world problem-solving.

NYCDSA’s full-time bootcamp focused on machine learning and data analytics in Python and R. The three-month program included units on data analytics and visualization, machine learning and big-data computing.

I completed several projects, building a portfolio of projects to showcase for potential employers. For one of my projects, I web-scraped stock pitches from a crowd-sourced financial research website and experimented with natural language processing (NLP) techniques to develop an investment strategy. My capstone project was focused on using macroeconomic time-series data to develop composite indicators with the potential to be used in a style rotation investment strategy. In addition, I built an R Shiny data-visualization application and completed a Kaggle competition as part of a group.

I thought learning Python very well was particularly helpful. While doing so, I was exposed to a variety of new machine-learning techniques, such as support vector machines (SVM), tree models, clustering methods and deep-learning algorithms. NYCDSA's instructors taught these techniques from both theoretical and practical implementation perspectives, utilizing tools such as Pandas, Matplotlib, Scikit-learn and Tensorflow.

In my current role as a research portfolio analyst on the derivatives team at quantitative investment firm Analytic Investors, I use quite a few of the techniques and tools I learned at the NYCDSA bootcamp. I’m very glad I was exposed to a wide variety of data-science tools, because I’ll often try to find new ways to approach and assess more open-ended ideas.

Long-term success as an investor and a data scientist is dependent on your ability to adapt and stay open-minded.

All students could benefit from connecting with many people across different industries and speaking with them about their careers, and the bootcamp also offered networking opportunities. Informational interviews allow you to learn the practical aspects of the job and will leave you with a much better sense of what the career entails.

Finding proper guidance, keeping an open mind and being willing to take risks while you’re young are all important steps towards realizing a career in quant investing or data science on the buy side.

Aarsh Sachdeva is a quantitative researcher and portfolio analyst on the derivatives team at Analytic Investors, a quantitative, factor-based investment firm with $20bn under management was acquired by Wells Fargo Asset Management a little over a year ago. While finishing his degree at Georgetown University, Sachdeva worked as a summer analyst at Accenture and Millennium Management. He completed the NYC Data Science Academy Bootcamp.

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