Staff Data Scientist, ML (Quantitative Researcher)

About the position

Robinhood is building the future of finance with a mission to democratize finance for all. The Investment Strategy team is expanding access to sophisticated investment advice by turning high-quality portfolio management into a scalable, intuitive experience for everyday customers. This team combines rigorous investment thinking with a mobile-first product design, dedicated to building accessible investing tools that reach and support millions of people. As a Staff Data Scientist, ML, you will report to the Chief Investment Officer and play a key role in shaping our investment product offerings. In this role, you will build complex portfolio construction factor models, identify critical areas for methodology improvement, and design high-performing investment solutions. You will also collaborate closely with Product, Engineering, Compliance, and Legal to launch these products and bring them directly to our customers. This role is based in our New York, NY office, with in-person attendance expected at least 3 days per week. Robinhood believes in the power of in-person work to accelerate progress, spark innovation, and strengthen community.

Responsibilities

  • Partner with the Chief Investment Officer and investment leadership to develop, refine, and maintain complex portfolio construction models.
  • Work with large datasets, including unconventional data sources, to predict and test statistical market patterns, conceptualize valuation strategies, and improve mathematical models.
  • Backtest and implement financial models and signals in a live trading environment.
  • Continuously research and analyze new approaches to building portfolio models, applying knowledge of existing and emerging quantitative finance principles, theories, and techniques to inform investment decisions.
  • Partner with product and engineering teams to help design and execute on complex projects and product launches.
  • Assist with reviews by Legal & Compliance to develop written policies and procedures when new features are introduced or methodology changes are developed.

Requirements

  • 5+ years of quantitative research, quantitative portfolio construction, or machine learning experience, ideally within asset management, a broker-dealer, an RIA, or broader financial services.
  • Experience taking machine learning models, research, or quantitative signals into a live investing or production environment.
  • Strong proficiency in Python and SQL for quantitative modeling, statistical analysis, and working with large-scale datasets.
  • Strong grounding in statistics, machine learning algorithms, and pattern recognition.
  • Outstanding communication skills, with a proven ability to translate complex modeling, statistical, or investment concepts for software engineering, product, and compliance partners.
  • Comfort with ambiguity, high personal ownership, and the ability to work independently to deliver on critical business milestones.

Benefits

  • Performance driven compensation with multipliers for outsized impact
  • bonus programs
  • equity ownership
  • 401(k) matching
  • 100% paid health insurance for employees with 90% coverage for dependents
  • Access to the best AI tools on the market and continuous AI skill-building for every employee, technical or not.
  • Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more.
  • Employer-paid life & disability insurance
  • fertility benefits
  • mental health benefits
  • company holidays
  • paid time off
  • sick time
  • parental leave
  • Catered meals
  • events
  • comfortable workspaces
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Common Interview Questions And Answers

1. HOW DO YOU PLAN YOUR DAY?

This is what this question poses: When do you focus and start working seriously? What are the hours you work optimally? Are you a night owl? A morning bird? Remote teams can be made up of people working on different shifts and around the world, so you won't necessarily be stuck in the 9-5 schedule if it's not for you...

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