Manager, Data Science, Payments & Subscriptions

PlayStation•San Diego, CA

4d•Hybrid

About The Position

The Data Science Manager leads a focused team improving how players pay, subscribe, and recover from payment failures. The role owns the team’s scientific roadmap, develops data scientists, and partners with product, engineering, finance, marketing, and operations to turn ambiguous business questions into clear recommendations supported by trustworthy evidence. It is a technically engaged first-line management position, with responsibility for reviewing experiment designs, metrics, models, analytical code, and recommendations; guiding the team through uncertainty and business tradeoffs; and contributing directly to selected complex problems. The initial portfolio includes payment method performance, payment acceptance, routing and retry strategies, subscription recovery, and measurement of experiments and lifecycle interventions. The manager helps partners understand incremental value, customer friction, processing and operational costs, and risk guardrails. Scope may expand across adjacent Direct to Consumer business areas as priorities evolve. The Direct to Consumer 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. It works with product, engineering, finance, marketing, and operations teams to use experimentation, forecasting, modeling, and trustworthy measurement to improve decisions and player experiences.

Requirements

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 formal technical leadership experience with a strong 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 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.

Nice To Haves

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.

Responsibilities

Lead, hire, onboard, and develop a team of data scientists supporting commerce, payments, subscriptions, and lifecycle work.
Own the team’s roadmap, intake, prioritization, capacity planning, and stakeholder alignment; clarify ownership and tradeoffs to focus delivery on important commitments.
Partner with business and technical leaders to define decisions, hypotheses, success metrics, measurement plans, and recommendations for action.
Guide experimentation, causal inference, forecasting, and modeling work to improve 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 in 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.