Staff Data Scientist, Algorithm

About the team + role

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

The Incentives Data Science team sits at the intersection of Product, Marketing, Finance and Machine Learning. Our mission is to enable sustainable, data-driven growth by building modeling, measurement, and optimization systems that drive activation, retention, and revenue at scale. We partner closely with cross-functional teams across Robinhood to design, evaluate, and operationalize incentive programs that efficiently acquire, activate, and retain customers.

As a Staff Data Scientist, Incentives, you will lead the end-to-end design, optimization, and evolution of Robinhood’s incentive systems. You’ll build predictive and causal ML models, design experimentation frameworks, and develop decisioning and allocation algorithms that directly influence how millions of users engage with Robinhood. This is a rare opportunity to own highly impactful ML systems while shaping incentive strategy at company scale!

This role is based in our Menlo Park, CA office, with in-person attendance expected at least 3 days per week.

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.

What you’ll do

• Build, deploy, and iterate on predictive and causal models for incentive targeting
  


• Design and evaluate experiments to measure incremental impact, payback, and ROI of promotional programs
  


• Partner cross-functionally with Product, Finance, Marketing, and Engineering to inform scalable incentive strategies
  


• Design and optimize incentive allocation algorithms under budget and policy constraints to maximize incremental impact
  


• Monitor production systems, analyze user behavior, and propose algorithmic and policy improvements
  


• Influence the long-term vision for growth modeling at Robinhood, including personalization and cross-sell optimization
  

What you bring

• 7+ years of experience applying ML in production, ideally in growth, incentives, marketplace, or personalization domains
  


• Proven track record owning models end-to-end — from design and development to deployment and iteration
  


• Deep expertise in predictive modeling, uplift modeling, causal inference, and experimentation design
  


• Strong technical skills in Python, SQL, and ML frameworks (e.g., scikit-learn, XGBoost, PyTorch or TensorFlow)
  


• Experience working with Airflow and production pipelines, as well as tools for causal ML and experimentation
  


• Excellent product intuition and ability to translate ambiguous goals into measurable algorithmic solutions
  


• Strong communication and stakeholder management skills, with the ability to influence across teams
  


• A collaborative and growth-minded approach, with strong technical leadership and a consistent focus on impact and ROI
  

What we offer

• Challenging, high-impact work to grow your career
  


• Performance-driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
  


• Best in class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents
  


• Lifestyle wallet – a highly flexible benefits spending account for wellness, learning, and more
  


• Employer-paid life & disability insurance, fertility benefits, and mental health benefits
  


• Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
  


• Exceptional office experience with catered meals, events, and comfortable workspaces