Description
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.
About the Role
We are seeking a Staff Product Manager, with a focus in Analytics, Experimentation, and CLV/LTV modelling, to lead the strategy, prioritization, and execution of AI/ML capabilities and products that drive business decision-making.
This role operates at the intersection of business, product, data science, and engineering, and is responsible for leading 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 role goes beyond squad ownership to drive alignment across teams, establish best 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 and leveraged across 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.
- Identify opportunities 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 with other Product Management teams and cross-functional leaders to operate as a unified team to deliver cohesive strategies and stakeholder communication.
- Align Analytics strategy, prioritization, and execution through collaboration with the Integrated Analytics Partners, Data Science leadership, and Engineering leadership.
- 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 when beneficial.
Stakeholder Engagement
- Engage with business stakeholders to understand needs, gather feedback, communicate progress, and clarify how analytics and ML outputs inform player value understanding.
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