Staff Data Scientist - Quantitative Research for AI

Unchain Data; New York, Northern (NY, KY); Hybrid; USD 217,000 - 255,000; Full time

Benefits offered by this job

100% paid health insurance

Equity ownership and bonus programs

401(k) matching

Excellent office and meals

Job summary

Robinhood is seeking a Staff Data Scientist (Quantitative Researcher) in New York, NY to shape investment products by building advanced portfolio construction models and testing new methodologies. You will partner with the CIO and cross‑functionally with Product, Engineering, Compliance, and Legal to bring sophisticated signals to everyday customers.

You will work with large datasets and unconventional sources, backtest models in live trading environments, and translate complex quantitative

Qualifications

• 5+ years of quantitative research or portfolio construction experience in financial services.
• Experience deploying ML models or signals in live investing environments.
• Proficient in Python and SQL for modeling and data analysis.
• Strong grounding in statistics and ML algorithms.
• Excellent communication to translate concepts to engineering, product, and compliance.

Responsibilities

• Develop and maintain complex portfolio construction models with CIO.
• Work with large datasets and unconventional data sources to forecast market patterns.
• Backtest and implement financial models in live trading.
• Research new approaches to building portfolio models and apply QM principles.
• Collaborate with product and engineering on projects and launches.
• Support Legal & Compliance reviews for new features.

Skills

Quantitative research

Portfolio construction

Python

SQL

Statistics

Communication

Independent work

Job description

Robinhood is seeking a Staff Data Scientist (Quantitative Researcher) in New York, NY to shape investment products by building advanced portfolio construction models and testing new methodologies. You will partner with the CIO and cross‑functionally with Product, Engineering, Compliance, and Legal to bring sophisticated signals to everyday customers.

You will work with large datasets and unconventional sources, backtest models in live trading environments, and translate complex quantitative