Data Science Manager, Fraud & Payments

Why PlayStation

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Data Science Manager Fraud & Payments

San Diego CA or Remote (US only considered)*
PlayStation Store Publishing Operations & Commerce (SPOC)

Who We Are

PlayStation operates one of the largest digital commerce platforms in the world. The Fraud & Payments Data Science team protects the PlayStation Network from abuse while enabling seamless secure purchasing experiences for millions of players. We build and maintain machine learning systems that evaluate risk in real time optimize authorization performance detect emerging fraud patterns and drive measurable business impact across the payments ecosystem.

About the Role

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 teams technical maturity roadmap and long-term capabilities.

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

Qualifications

Required

• 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

Preferred

• 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

**PlayStation offers a global hybrid office/remote working model Flex Modes which aims to combine the flexibility of remote working with the magic of in-person collaboration. The model is built upon the principle of working together in-person as work requires with the freedom to work remotely otherwise.

Equal Opportunity Statement:

Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity gender expression and gender reassignment) race (including colour nationality ethnic or national origin) religion or belief marital or civil partnership status disability age sexual orientation pregnancy maternity or parental status trade union membership or membership in any other legally protected category.

We strive to create an inclusive environment empower employees and embrace diversity. We encourage everyone to respond.

PlayStation is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.