Staff Data Science Product Manager

Why Sony Interactive Entertainment

Sony Interactive Entertainment isnt just the Best Place to Play its also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation were 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.

About the Role

We are seeking a Staff Product Manager with a focusinAnalytics Experimentation and CLV/LTV modelling to lead the strategy prioritization and execution of AI/ML capabilities and products that drive business decision-making.

This roleoperatesat the intersection of business product data science and engineering andis responsible forleading high-impact problem spaces that span teams while improving the effectiveness consistency and scalability of analytics product management practices. This includes technical ML solutions that help the business understand where player value is coming from and how it changes over time. The role is also responsible for creating the conditions for high-performing Data Science and ML teams to deliver quality production-grade products that drive measurable business value.

As a Staff-level individual contributor this rolegoes beyond squad ownership to drive alignment across teamsestablishbest practices and influence portfolio-level decisions. You will partner closely with Integrated Analytics Partners Data Science leadership and Engineering to ensure that analytics investments are coordinated scalable and focused on the highest-impact opportunities.

This role is critical to enabling a cohesive product-driven analytics ecosystem where work is not only delivered effectively within squads but also aligned andleveragedacross the organization including value modelling and player understanding use cases.

Key Responsibilities

Analytics Product Strategy & Lifecycle Ownership

• Lead discovery and definition of ambiguous high-impact AI/ML problem spaces that require coordination across teams including applications that improve understanding of player value and value drivers.

• Drive alignment across squads to ensure coordinated execution and avoid duplication of effort.

• Identifyopportunities to scale solutions reuse components and standardize approaches across analytics experimentation forecasting and value modelling use cases.

• Lead product thinking across the end-to-end ML lifecycle from opportunity framing and evaluation design through deployment monitoring iteration and long-term value realization.

Prioritization & Roadmap Management

• Own prioritization across multiple squads balancing business impact feasibility technical maturity adoption potential and resource constraints.

• Partner with Integrated Analytics Partners and senior Data Science and Product leaders to align work to business strategy.

• Help shape how analytics work is sequenced and balanced across new feature development operationalization and productization including roadmaps for technical ML teams.

Experimentation & Decision Frameworks

• Partner with Data Science leaders to ensure statistical rigor and methodological consistency across experimentation modelling forecasting and player value analysis.

• Drive adoption of experimentation and value-based analytical techniques as core decision-making tools across business functions.

Cross-Functional Leadership

• Partner closely withother Product Management teams and cross-functional leaders tooperateas a unified team to deliver cohesive strategies and stakeholder communication.

• Align Analytics strategy prioritization and execution through collaboration withtheIntegratedAnalytics Partners Data Science leadership and Engineeringleadership.

• Coordinate work across multiple squads to deliver integrated analytics solutions.

• Influence stakeholders across functions to drive alignment and execution.

Scaling & Adoption

• Drive thinking around scalability reuse and long-term sustainability of analytics solutions particularly where shared capabilities can improve understanding of player value.

• Partner with AI/ML Engineering to transition high ROI high SLA capabilities into scalable production-grade systems.

• Define success criteria for analytics and ML products including business impact adoption reliability interpretability and operational sustainability.

• Ensure successful adoption of AI/ML capabilities by end users.

• Ensure analytics and ML capabilities are embedded into business workflows and decision-making processes so that technical outputs translate into durable operational impact.

• Advocate for investments in shared capabilities and platforms whenbeneficial.

Stakeholder Engagement

• Engage with business stakeholders to understand needs gather feedback communicate progress and clarify how analytics and ML outputs inform player value understanding.

• Support Integrated Analytics Partners in translating strategic priorities into actionable work.

• Communicate outcomes and impact of analytics initiatives clearly and effectively including how they support business understanding of value growth and customer lifecycle dynamics.

Standards & Best Practices

• Define and promote best practices for analytics product management including prioritization experimentation and lifecycle management.

• Mentor and support other Analytics Experimentation & Product Leads.

• Identifygaps in how work progresses through the lifecycle and drive improvements.

• Raise the overall quality and consistency of work across teams.

• Create the conditions for high-performing Data Science and ML teams by clarifying priorities reducing delivery friction and strengthening cross-functional ways of working across discovery development deployment and adoption.

Qualifications and Education Requirements

• Bachelors degree in Business Data Science Computer Science ora relatedfield.

• 12 years of relevant experience including 6 yearsindigital product management.

• Proven experience leading complex cross-functional initiatives involving data science and engineering teams.

• Proven experience partnering with high-performing Data Science ML and Engineering teams to ship production-grade products that deliver measurable business outcomes.

• Strong understanding of data science workflows including experimentation modeling forecasting value analysis andproductionization.

• Demonstrated ability tooperatein highly ambiguous environments and drive alignment across teams

• Experience influencing prioritization and decision-making across multiple teams or domains

• Experience in Agile product management methodologies and working with cross-functional squads.

• Strong communicationand stakeholder management skills including working with senior leaders

• Strong mentorship skills and experience elevating the capabilities of other product managers or analytics leaders.

Preferred Skills

• Proven ability to evaluate tradeoffs in scaling AI/ML solutions balancing model sophistication with reliability performance interpretability and operational complexity in production environments.

• Experience defining quality standards and success metrics for analytics and ML products including adoption reliability interpretability and business impact.

• Familiarity with analytics and data tools such as SQL Python or similar.

• Experience with experimentation and measurement frameworks (A/B testing causal inference incrementality) or related analytical approaches such as forecasting scenario modeling CLV/LTV modelling and model evaluation.

• Ability tooperateeffectively in fast-paced environments and manage multiple initiatives simultaneously.

• Experienceidentifyingopportunities for reuse platform development and scaling analytics capabilities.

• Demonstrated ability to partner effectively with other Product Management teams and cross-functional leadersoperatingas a unified team to deliver cohesive strategies and stakeholder messaging.

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 ourCandidate Privacy Noticefor 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.