Senior Data Scientist, ML (Brokerage)

Robinhood

Hybrid

Quick summary

Work type: Hybrid
Location: Menlo Park, CANew York, NY
Salary: $187,000–$220,000 / yr
Posted: 59 days ago
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $190k

This role $204k

$152k most similar roles pay here $233k

This role pays more than 63% of similar roles. Most pay $159,450–$220,900 — the shaded band above. At the midpoint, this role pays about $204k versus about $190k for comparable roles.

Based on 240 similar postings.

Employer

About Robinhood

Robinhood is a financial technology company offering commission-free stock, ETF, options, and cryptocurrency trading through its mobile app, aimed at democratizing access to financial markets. Industry: Financial Technology & Investment App

Robinhood currently has 83 open roles on FindRole.

Listed pay typically runs $187,000–$220,000 across 82 roles with salary data.

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At a glance

TL;DR · Senior Data Scientist, ML (Brokerage)

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As a Senior Data Scientist, ML on Robinhood’s Brokerage team, you will lead the development of recommendation systems for prediction markets and other product areas. You’ll work closely with product managers and engineers to identify opportunities for personalization, design modeling approaches, and implement solutions that enhance customer engagement. Your daily tasks include building algorithms to personalize user experiences, collaborating with software and machine learning engineers on scalable feature pipelines, and conducting experiments to evaluate model performance. Proficiency in Python, SQL, and machine learning systems is essential, along with experience in experimentation methods and causal inference. This role focuses on a fast-growing area within Robinhood’s platform, aiming to improve how customers interact with prediction markets and beyond.

Skills

Python SQL machine learning systems production modeling experimentation methods causal inference CI/CD

What you'll do

Lead the development and improvement of recommendation system algorithms for prediction markets.
Identify opportunities for personalization in customer experiences with product managers.
Design and implement scalable feature pipelines and ranking systems with engineering teams.
Develop models to enhance relevance and user interaction within prediction markets offerings.
Conduct experiments to evaluate model performance and measure impact on customer engagement.

What we're looking for

5+ years of experience building recommendation systems in customer-facing products.
Proficient in Python and SQL with strong machine learning production modeling experience.
Experience with experimentation methods and causal inference for model evaluation.
Clear communication and effective collaboration skills with cross-functional teams.
Demonstrated ability to drive projects from concept through implementation.

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