Staff Data Scientist, Incentives (ML)

Unchain Data; Menlo Park (CA); On-site; USD 217,000 - 255,000; Full time

Benefits offered by this job

Health insurance

Equity ownership

401(k) matching

Wellness wallet

Flexible benefits spending

Life and disability insurance

Fertility benefits

Mental health benefits

Paid time off

Catered meals

Office events

Job summary

Robinhood is seeking a Staff Data Scientist, Incentives, to 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 role is based in our Menlo Park, CA office, with in-person attendance expected at least 3 days per week.

Qualifications

• 7+ years of ML in production, preferably growth or incentives
• Proven track record owning models end-to-end
• Deep expertise in predictive modeling, uplift modeling, causal inference, and experimentation design
• Strong Python, SQL, and ML frameworks (scikit-learn, XGBoost, PyTorch, or TensorFlow)
• Experience with Airflow and production pipelines, plus causal ML and experimentation
• Excellent product intuition and ability to translate goals into measurable algorithms
• Strong communication and stakeholder management to influence across teams
• Collaborative, growth-minded, with technical leadership and ROI focus

Responsibilities

• 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
• Monitor production systems, analyze user behavior, and propose algorithmic and policy improvements
• Influence the long-term vision for growth modeling including personalization and cross-sell optimization

Skills

ML in production

End-to-end models

Predictive modeling

Uplift modeling

Causal inference

Experiment design

Python

SQL

Airflow

Cross-functional collaboration

Tools

scikit-learn

XGBoost

PyTorch

TensorFlow

Job description

About Us

Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you're ready to be at the epicenter of this historic cultural and financial shift, we'd love to have you join us in building the future of finance.

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, and builders who are wired to make an impact. Robinhood is 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. We believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community.

Responsibilities

• 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

Requirements

• 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

Benefits

• 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 and 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

Compensation

In addition to the base pay range listed below, this role is also eligible for bonus opportunities, equity, and benefits.

Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands. The expected base pay range for this role is based on the location where the work will be performed and is aligned to one of three compensation zones.

Zone 1 (Menlo Park, CA; New York, NY; Bellevue, WA; Washington, DC): $217,000 – $255,000 USD

Zone 2 (Denver, CO; Westlake, TX; Chicago, IL): $191,000 – $224,000 USD

Zone 3 (Lake Mary, FL; Clearwater, FL; Gainesville, FL): $170,000 – $199,000 USD

For other locations not listed, compensation can be discussed with your recruiter during the interview process.