Data Science Manager, Fraud & Payments

Sony Playstation Network•San Diego, CA

45d•$186,000 - $279,000•Hybrid

About The Position

We are hiring a Data Science Manager to lead the data scientists who develop fraud detection models, optimize payment outcomes, build standardized experimentation frameworks, and drive operational insights across the commerce lifecycle. You will partner closely with Fraud Operations, Payments Strategy, Finance, and Engineering to balance fraud prevention, revenue growth, and frictionless customer experience. This role is hands-on. You will guide modeling and analysis work directly, while also developing the team's technical maturity, roadmap, and long-term capabilities.

Requirements

8+ years of experience building and deploying machine learning models in production, ideally in fraud, trust & safety, risk scoring, or payments
2+ years of demonstrated leadership of data scientists (formal management or senior technical leadership)
Strong Python and SQL, and experience with ML toolkits (scikit-learn, XGBoost, etc.)
Hands-on experience evaluating risk controls using experimentation, holdouts, or causal inference methods
Ability to navigate tradeoffs between fraud loss, authorization success, revenue impact, and user friction
Ability to simplify complex findings and influence product and operations decisions

Nice To Haves

Experience with real-time scoring systems or streaming data architectures
Background in e-commerce, fintech, gaming, or digital marketplaces
Experience with causal inference frameworks in applied / non-ideal conditions
Familiarity with LLM classification or graph-based detection approaches

Responsibilities

Lead, coach, and develop data scientists working on fraud detection, risk modeling, and payment optimization
Hire and grow talent; set clear expectations and provide ongoing career development and performance feedback
Design, evaluate, and productionize machine learning models for real-time decisioning and post-transaction review
Build standardized processes for experimentation, including champion vs challenger model evaluations, holdouts, and policy impact testing
Apply causal inference to measure the incremental impact of controls and payment routing strategies
Develop automated monitoring, anomaly detection, and alerting for emerging fraud patterns and behavior shifts
Establish predictable operating rhythms with Fraud Ops, Payments, and Engineering to align objectives and drive execution
Drive adoption of shared metrics, dashboards, and measurement frameworks across stakeholder teams