Manager, Data Science, Payments & Subscriptions
Sony Interactive Entertainment Global · United States, San Diego, CA
About this role
**Why Sony Interactive Entertainment?** Sony Interactive Entertainment (SIE) isn’t just the Best Place to Play—it’s also the Best Place to Work. As the company behind the PlayStation brand, SIE delivers cutting-edge hardware and network services to 100M+ people and is home to beloved, recognizable IP. **About the Team** The Direct to Consumer (D2C) Data Science organization brings together Data Science, Data Engineering, and ML Engineering to support PlayStation’s digital business across commerce, subscriptions, payments, lifecycle experiences, and player-facing services. The team partners with product, engineering, finance, marketing, and operations to turn experimentation, forecasting, modeling, and trustworthy measurement into better decisions and better player experiences. **About the Role** SIE is seeking a **Data Science Manager** to lead a focused team improving how players pay, subscribe, and recover from payment failures. You’ll own the team’s scientific roadmap, develop data scientists, and partner cross-functionally to turn ambiguous business questions into clear recommendations backed by trustworthy evidence. This is a technically engaged first-line management role. You’ll review experiment designs, metrics, models, analytical code, and recommendations—help the team reason through uncertainty and business tradeoffs, and contribute directly to selected complex problems. **Initial portfolio includes:** payment method performance, payment acceptance, routing and retry strategies, subscription recovery, and measurement of experiments and lifecycle interventions. **What you’ll be doing** - Lead, hire, onboard, and develop a team of data scientists supporting a defined commerce, payments, subscriptions, and lifecycle portfolio. - Own the team’s roadmap, intake, prioritization, capacity planning, and stakeholder alignment. - Partner with business and technical leaders to define decisions, hypotheses, success metrics, measurement plans, and recommendations. - Guide experimentation, causal inference, forecasting, and modeling to improve payment and subscription decisions—balancing value, customer experience, costs, and risk guardrails. - Review experiment designs, analytical code, data quality, metric definitions, model assumptions, uncertainty, and recommendations; contribute to high-priority/complex problems. - Coach data scientists to strengthen scientific judgment, stakeholder influence, and communication; build independent ownership through delegation and clear expectations. - Set standards for reproducible analysis, documentation, reusable workflows, and results communication; partner with Data Engineering and ML Engineering on dependencies. - Communicate recommendations, expected impact, confidence, limitations, and next actions so partners can make informed decisions. **What we’re looking for** - 6+ years of industry experience in data science, applied statistics, machine learning, or a related quantitative field (including experimentation, causal inference, measurement, or model development). - 2+ years of people management experience (or equivalent formal technical leadership with a strong track record of mentoring/coaching and driving outcomes). - Strong background in experimentation, causal inference, metric design, and decision science for digital products or consumer businesses. - Deep fluency with **SQL** and **Python**, with the technical depth to review analytical code, challenge assumptions, guide experiment/m
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