Staff Data Scientist, Robinhood
Algorithms
$217-255k
+ Multipliers for outsized impact, bonus programs, equity ownership
3+ days a week in office (Menlo Park, CA)
Commission-free stock trading & investing app
Job no longer available
Commission-free stock trading & investing app
1001+ employees
Job no longer available
$217-255k
+ Multipliers for outsized impact, bonus programs, equity ownership
3+ days a week in office (Menlo Park, CA)
1001+ employees
Company mission
To democratize finance for all.
Role
Who you are
- 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 the job involves
- 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!
- 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
Our take
For years, traditional brokers ruled the share trading realm, charging hefty fees that locked out everyday investors. Robinhood emerged as a disruptor, offering commission-free trading and opening the door to a new generation. Since then, expectations have continued to evolve, with investors looking not just to lower costs, but for simpler tools, clearer information, and guidance that makes the market easier to understand.
Robinhood was built to meet those changing expectations, designed from the start as a mobile-first platform that makes investor more accessible and less intimidating for people at every stage of their financial journey. Through its app, users can trade stocks and other financial instruments while benefiting from a clean interface and built-in educational support, helping them move from curiosity to confidence without unnecessary complexity.
Recently, Robinhood has strenghtned its impact too, particularly in the UK. With the launch of AI-powered Digests within its Robinhood Cortex assistant that helps users understand what's driving stock movements in plain English, while upcoming options trading and expanded educational resources add flexibility and depth. Together, these steps position Robinhood as a platform evolving alongside its users, blending technology and education to stay ahead in an ever-changing investment landscape.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 13 rounds)
Feb 2021
$2.4bn
CONVERTIBLE
Jan 2021
$1bn
LATE VC
Total funding: $5.6bn
Company HQ
Downtown Menlo Park, Menlo Park, CA
Leadership
Vlad Tenev
(Co-Founder, CEO & Chairman)Studied Mathematics at Stanford University, where they met Baiju. Dropped out of a PhD to start Robinhood. Alongside Robinhood, Vlad is also the co-founder and Executive Chairman of Harmonic.
Baiju B.
(Co-Founder)Studied Mathematics and Physics at Stanford University, where they met their Vlad and co-founded Robinhood in 2012. Baiju is also the Founder and CEO of Aetherflux.
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