Student Perspectives

Student Perspective

Jules Landry-Simard

Jules Landry-Simard

MFE 18

Data Scientist, OVM Pricing
Opendoor
San Francisco, CA

Job in Data Science Delivers Positive Impact

I chose the Berkeley MFE program specifically for its tech connections. I knew I wanted a position where I worked with an actual product that would have a positive impact on people's lives. Opendoor has that.

Tech connections open doors for data scientists

My team at Opendoor comprises both data scientists and engineers, and three of us are Berkeley MFEs. We develop and implement automated pricing models for home valuation. When a seller comes to the Opendoor website, our algorithms calculate the value of their home, and we can instantly make them an offer to buy it. The seller realizes an immediate sale, and Opendoor can go on to renovate and put the house back on the market.

Career services with broad connections

The career services staff pulls together an amazingly diverse array of high-quality job offers, on the sell and buy side, in private equity and technology. There is something for everyone.

I first encountered both AlphaSimplex, where I did my internship, and Opendoor, where I work now, at the Career Fair on campus. I easily got interviews with both. The career services office helped out with assessing and, eventually, negotiating the offers.

Rigorous curriculum with real-world applications

The diversity of the curriculum prepares you for so many different career paths. It exposes you to data science and derivatives, cryptocurrency and fixed income assets.

The Machine Learning course is very practical. It gave me tools and taught me when and how to use them to best advantage. I use this knowledge all the time on the job.

Teamwork builds on efficiency and expertise

The pace of the program demands that every team project get underway quickly. You are always working under time constraints. This means you have to be familiar with your own strengths and weaknesses, as well as those of your teammates.

At the end of a project, it’s important to examine what worked well and what didn’t. That is how you learn to build on efficiencies and expertise.

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