Senior Data Scientist, Risk and Fraud
- Menlo Park, CA
Join a leading fintech company that’s democratizing finance for all.
Robinhood was founded on a simple idea: that our financial markets should be accessible to all. With customers at the heart of our decisions, Robinhood is lowering barriers, removing fees, and providing greater access to financial information. Together, we are building products and services that help create a financial system everyone can participate in.
Just as we focus on our customers, we also strive to create an inclusive environment where our employees can thrive and do impactful work. We are proud of the world class products and company culture we continue to build and have been recognized as:
- A Great Place to Work
- A CNBC Disruptor 50 in 2019 and 2020
- A LinkedIn Top Startup in 2017, 2018, 2019 and 2020
Robinhood is backed by leading investors that include DST Global, Index Ventures, NEA, Ribbit Capital, Thrive Capital, and Sequoia.
Check out life at Robinhood on The Muse!
About the role:
Insights from data power most decisions at Robinhood and our company trajectory is defined by the systems, tools, and analytics powered by this exceptional team. As a Data Scientist working on Risk you would work with engineers, product managers and operations teams across the company to understand and mitigate the risks to our business.
Robinhood faces unique data challenges with a focus on integrating multifaceted data streams such as rapidly changing market data, user data based on app activity, and brokerage operations data to understand user behavior and the risks to our business.
We are looking for experienced Data Scientists to help detect and reduce risk to Robinhood - a crucial role to our business and customers. The ideal candidate is passionate about understanding the different risk vectors at a fast-growing company and building solutions to mitigate these risks. This team is part of the larger Data Team here at Robinhood.
What you’ll do day-to-day:
- Combining knowledge of several research domains to improve our understanding of different risks to Robinhood and help power decisions
- Designing new machine learning systems to power the fraud prevention and risk reduction efforts at Robinhood especially in product areas
- Build production grade models on large-scale datasets to measure effectiveness across products by leveraging statistical modeling, machine learning and data mining techniques.
- Collaborate with the rest of the data team and partner marketing, product, content, design teams to build data solutions and products to drive user and revenue growth.
- Work with cross-functional teams to implement insights and analytical solutions to empower data-driven decision making.
- Problem solving skills and a can-do attitude to dive deep into data to solve business problems
- Graduate degree in a quantitative field such as mathematics, statistics, computer science, engineering or natural sciences (or equivalent research/work experience)
- Solid understanding of unsupervised learning, statistical analysis and machine learning algorithms for imbalanced datasets.
- Excellent programming skills, including expert level familiarity with either Python or R programming languages
- At least 4 years of work experience, either in a research/ academic or commercial/ industry setting.
- Risk & Fraud experience in fintech is a plus
- Passion for working and learning in a fast-growing company
We’re looking for more growth-minded and collaborative people to be a part our journey in democratizing finance for all. If you’re ready to give 100% in helping us achieve our mission—we’d love to have you apply even if you feel unsure about whether you meet every single requirement in this posting. At Robinhood, we're looking for people invigorated by our mission, values, and drive to change the world, not just those who simply check off all the boxes.
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