Data Scientist
Robinhood Menlo Park, CA, USA $100k – $170k Long term > 2 years
About Robinhood

Robinhood is changing the way America invests. We believe our financial system should work for everyone and not just a few. We offer commission-free trading for stocks, ETFs, options, and cryptocurrencies—all in one, user-friendly platform. Since our public launch in 2015, we’ve enabled millions of people to participate in the markets, cementing us as the fastest-growing brokerage ever. 

Job Description

Insights from data power most decisions at Robinhood. A Data Scientist at Robinhood works with other data scientists and engineers to collect, organize, and analyze data from all parts of Robinhood's business to power these decisions.

Every decision made at Robinhood is backed by data. Our company trajectory is defined by the systems, tools, and analytics powered by our exceptional team. We integrate multifaceted data streams such as rapidly changing market data, user data based on app activity, and brokerage operations data to perfect our processes and workflows.


As a Data Scientist you'll:

  • Combine knowledge of several research domains to help power decisions at Robinhood.
  • Design new machine learning systems to power the analytics efforts at Robinhood.
  • Improve the statistical methodology backing our experimentation platform.
  • Build statistical models to predict user behavior and risks to the business.
  • Adapt machine learning algorithms to solve problems across several teams, such as product, operations, customer support, compliance, and security.

Requirements

Some things we consider critical to being a Data Scientist: 

  • Graduate degree in a quantitative field such as mathematics, statistics, engineering or natural sciences (or equivalent research experience).
  • Solid understanding of statistical analysis and machine learning algorithms.
  • Excellent programming skills, including expert level familiarity with either Python or R programming languages.
  • At least 2 years experience of working in a research setting in academic or commercial setting.

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