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Closed listing

Sony Interactive Entertainment (SIE)

Manager, Data Science, Payments & Subscriptions

San Diego, CA, United States

$186,200 - $279,200 USD

This opening has closed.

The original listing is kept below for reference.

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Job details

Pay: $186,200 - $279,200 USD
Posting language: English
Posted: Oct 30, 2026

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Original job description

Archived listing. The details below describe a past opening.

Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play - it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity.

Manager, Data Science, Payments & Subscriptions San Diego, CA remote considered* (flex mode) 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. 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.

About the Role 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.

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. 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.

What we're looking for:

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

Preferred Qualifications

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.

At SIE, we consider several factors when setting each role’s base pay range, including the competitive benchmarking data for the market and geographic location.

Please note that the base pay range may vary in line with our hybrid working policy and individual base pay will be determined based on job-related factors which may include knowledge, skills, experience, and location.

In addition, this role is eligible for SIE’s top-tier benefits package that includes medical, dental, vision, matching 401(k), paid time off, wellness program and coveted employee discounts for Sony products. This role also may be eligible for a bonus package. Click here to learn more.

The estimated base pay range for this role is listed below.

$186,200 - $279,200 USD

Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights.

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

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Skills

Data engineering
ML
Forecasting
Prioritization
Customer experience
Communication skills
Documentation
Statistics
Machine learning
Team leadership
Leadership
Coaching
SQL
Python

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