Senior Data Scientist, Fraud

Robinhood

Hybrid

Quick summary

Work type: Hybrid
Location: Menlo Park, CA
Salary: $187,000–$220,000 / yr
Posted: 109 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $168k

This role $204k

$116k most similar roles pay here $231k

This role pays more than 73% of similar roles. Most pay $126,800–$209,750 — the shaded band above. At the midpoint, this role pays about $204k versus about $168k 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 72 open roles on FindRole.

Listed pay typically runs $187,000–$220,000 across 72 roles with salary data.

Most-posted roles

View all roles at Robinhood

At a glance

TL;DR · Senior Data Scientist, Fraud

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As a Senior Data Scientist on the Fraud team at Robinhood, you will design and deploy machine learning solutions to detect and prevent fraud in real-time, ensuring user safety and regulatory compliance. Your responsibilities include analyzing behavioral data to identify new fraud patterns, developing robust data pipelines for model accuracy, and collaborating with engineering and product teams to implement security features. You should have 5+ years of experience in applied ML or data science, particularly in fraud detection, along with advanced skills in Python, SQL, and machine learning frameworks like XGBoost and TensorFlow. This role requires strong statistical knowledge, excellent communication abilities, and the capacity to work effectively in a fast-paced environment.

Skills

Python SQL XGBoost LightGBM TensorFlow ML A/B testing CI/CD Kafka Spark PostgreSQL Redis Docker Kubernetes Prometheus Grafana

What you'll do

Design and deploy real-time fraud detection models to protect users.
Analyze behavioral data to identify new fraud patterns and respond quickly.
Create robust data pipelines for model accuracy and reliability monitoring.
Partner with engineering teams to implement user safety features.
Guide long-term fraud prevention strategy through experimentation.

What we're looking for

5+ years of experience in data science or applied machine learning with a focus on fraud detection.
Advanced proficiency in Python and SQL, along with experience using ML frameworks like XGBoost, LightGBM, or TensorFlow.
Strong statistical skills including anomaly detection, pattern recognition, and A/B testing.
Excellent communication skills to influence decision-making across technical and non-technical audiences.
Collaborative mindset for working effectively in a fast-paced environment.

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