Manager, Data Science, Payments & Subscriptions

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. We partner closely with product, engineering, finance, marketing, and operations teams to turn experimentation, forecasting, modeling, and trustworthy measurement into better decisions and better player experiences.

We are looking for a Data Science Manager to lead a focused team improving how players pay, subscribe, and recover from payment failures. You will own the team's scientific roadmap, develop data scientists, and partner with product, engineering, finance, marketing, and operations to turn ambiguous business questions into clear recommendations supported by trustworthy evidence.

This is a technically engaged first-line management role. You will 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. Success means building a team that can independently deliver rigorous work, guide decisions, and improve payment and subscription outcomes.

The initial portfolio includes payment method performance, payment acceptance, routing and retry strategies, subscription recovery, and measurement of experiments and lifecycle interventions. You will help partners understand incremental value, customer friction, processing and operational costs, and risk guardrails. Over time, the scope may expand across adjacent D2C business areas as team priorities evolve.

• 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. Make ownership and tradeoffs clear so the team can deliver on its most important commitments.
• Partner with business and technical leaders to define the decisions to be made, hypotheses, success metrics, measurement plans, and recommendations for action.
• Guide experimentation, causal inference, forecasting, and modeling work that improves payment and subscription decisions. Evaluate business value alongside customer experience, costs, and risk guardrails.
• Review experiment designs, analytical code, data quality, metric definitions, model assumptions, uncertainty, and recommendations. Contribute directly to selected high-priority or technically complex problems.
• Coach data scientists to strengthen scientific judgment, stakeholder influence, and communication. Build independent ownership through delegation, constructive review, and clear expectations.
• Set practical standards for reproducible analysis, documentation, reusable workflows, and communication of results. Partner with Data Engineering and ML Engineering to address data and implementation dependencies.
• Communicate recommendations, expected impact, confidence, limitations, and next actions so partners can make informed decisions and evaluate outcomes.

• 6+ years of industry experience in data science, applied statistics, machine learning, or a related quantitative field, including work in experimentation, causal inference, measurement, or model development.
• 2+ years of people management experience, or equivalent experience in formal technical leadership with a strong track record of mentoring, coaching, and driving outcomes through other data scientists or machine learning practitioners.
• 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 and model design, and contribute directly to complex analyses.
• Proven ability to prioritize cross-functional work and communicate clear recommendations, confidence levels, tradeoffs, and next actions.
• Bachelor's degree or equivalent

• Experience in digital commerce, payments, subscriptions, billing, lifecycle, gaming, media, or scaled consumer technology businesses.
• Experience with experimentation platforms, trusted metric frameworks, or reusable data science tooling that improves consistency and decision quality.
• Experience managing senior individual contributors or helping data scientists expand scope, influence, and technical leadership.
• Familiarity with payment optimization, subscription lifecycle analytics, subscription recovery, payment method selection, pricing or offer measurement, forecasting, or customer retention modeling.
• Experience evaluating and improving AI-assisted analytical workflows, including validating outputs and preserving reproducibility and appropriate data handling.