Data Scientist, D2C Data Science

PlayStation Global•San Diego, CA

4h•$143,400 - $215,000•Hybrid

About The Position

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, payments, subscriptions, lifecycle experiences, and player-facing services. We partner closely with product, engineering, finance, marketing, operations, and data teams to turn experimentation, forecasting, modeling, and production-quality measurement into better decisions and better player experiences. We are looking for a Data Scientist to join a focused team within D2C Data Science supporting payment and subscription experiences across PlayStation’s direct-to-consumer business. This is a hands-on role for someone who can use statistics, machine learning, experimentation, and strong data judgment to help teams make better decisions about how players pay, subscribe, and move through global payment flows. The initial portfolio is expected to focus on payment method performance, payment flow optimization, subscription payment recovery, and ROI-based evaluation of experiments and business interventions. You will help teams understand customer behavior, payment success, cost and routing tradeoffs, and the business impact of new payment capabilities. Our team values practical scientific rigor: clear decision framing, trusted reusable metrics, transparent uncertainty, and recommendations that help teams move faster without sacrificing measurement quality. This role is best suited for someone who can independently own well-scoped analyses and models, work through ambiguity, and translate complex data into recommendations that improve customer experience and business performance.

Requirements

3+ years of professional experience in data science or machine learning
Bachelor's degree in statistics, mathematics, computer science, engineering, data science, or a related quantitative field or equivalent
Strong SQL and Python skills for data extraction, data validation, analysis, modeling, and reproducible workflows.
Solid foundation in statistics, experimental design, machine learning, predictive modeling.
Experience applying data science methods to ambiguous commercial, customer, payment, subscription, or operational problems.
Ability to communicate technical findings clearly to technical and non-technical partners.

Nice To Haves

Experience with digital commerce, payments, billing, subscriptions, fintech, marketplaces, gaming, media, or scaled consumer technology businesses.
Experience with payment method performance, authorization or success-rate analysis, payment optimization, routing or retry strategies, cost analysis, payment telemetry, or subscription recovery.
Experience designing, running, or analyzing experiments, including A/B tests, holdouts, quasi-experimental approaches, or causal inference methods.
Experience with forecasting, customer segmentation, churn / retention modeling, offer measurement, payment success modeling, subscription lifecycle analytics, or ROI-based business evaluation.
Experience working with large-scale data environments such as Snowflake, Databricks, Spark, BigQuery, or similar platforms, and familiarity with metric layers or source-of-truth datasets.

Responsibilities

Apply data science methods to high-impact questions across D2C payments, subscriptions, commerce, lifecycle, and player experience.
Design, analyze, and interpret A/B tests, holdouts, quasi-experimental analyses, and other measurement approaches with clear hypotheses, metrics, and decision criteria.
Analyze payment and subscription outcomes such as payment success, authorization performance, payment funnel behavior, routing or retry performance, cost tradeoffs, and subscription recovery.
Build statistical and machine learning models for forecasting, segmentation, propensity, retention, payment success, payment optimization, subscription outcomes, or offer performance.
Use SQL and Python to prepare data, validate assumptions, analyze behavior, and produce reproducible analytical workflows.
Partner with product, engineering, finance, marketing, operations, and data engineering teams to ensure analyses are technically sound, actionable, and operationally useful.
Communicate findings with clear recommendations, confidence levels, caveats, tradeoffs, next steps, and reusable documentation that supports better decision-making.