Robinhood is looking to detect and prevent fraud and abuse across its platform to safeguard its users and assets, reduce financial loss, and improve fraud detection precision.
Requirements
- Advanced proficiency in Python and SQL; experience with ML frameworks like XGBoost, LightGBM, or TensorFlow
- Strong statistical acumen with experience in anomaly detection, pattern recognition, and A/B testing
Responsibilities
- Design and deploy fraud detection models to protect Robinhood users and assets in real time
- Analyze behavioral data to uncover emerging fraud vectors and support rapid incident response
- Develop robust data pipelines and monitoring systems to ensure model accuracy and reliability
- Partner with engineering and product teams to implement safeguards and user-facing features
- Guide experimentation strategy and contribute to long-term fraud prevention roadmap
Other
- 5+ years of experience in data science or applied ML, with a focus on fraud detection or risk mitigation
- Excellent communication skills and ability to influence decision-making across technical and non-technical audiences
- A collaborative mindset and proactive approach to navigating ambiguity in fast-paced environments
- This role is based in our Menlo Park office(s), with in-person attendance expected at least 3 days per week.
