- As a Staff Data Scientist (Quantitative Researcher), 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
- At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams
- 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
Strong proficiency in Python and SQL for quantitative modeling, statistical analysis, and working with large-scale datasetsOutstanding communication skills, with a proven ability to translate complex modeling, statistical, or investment concepts for software engineering, product, and compliance partners5+ years of quantitative research, quantitative portfolio construction, or machine learning experience, ideally within asset management, a broker-dealer, an RIA, or broader financial servicesStrong grounding in statistics, machine learning algorithms, and pattern recognitionComfort with ambiguity, high personal ownership, and the ability to work independently to deliver on critical business milestonesExperience taking machine learning models, research, or quantitative signals into a live investing or production environment
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