PlayStation Global
Technology
DataScienceProductManager
Neural analysis suggests this role is
optimal for Staff candidates.
“Data Science Product Manager at PlayStation Global. Skills: Analytics, Experimentation, CLV modelling, AI/ML. Lead AI/ML capabilities strategy. Drive business decision-making”
What You'll Achieve.
Deliver measurable business outcomes; Drive measurable business value
Industry & Context.
Root cause analysis; Data-driven decision making
What They're Looking For.
Must Have
5+ years experience, Experience in Agile product management, Experience working with cross-functional squads, Experience influencing prioritization, Experience driving alignment across teams, Experience operating in highly ambiguous environments, Understanding of data science workflows
Nice to Have
Experience defining quality standards, Experience with experimentation and measurement frameworks, Experience with analytics and data tools, Ability to operate effectively in fast-paced environments, Experience identifying opportunities for reuse, Experience scaling analytics capabilities, Experience with CLV/LTV modelling, Experience with forecasting, Experience with model evaluation, Experience with causal inference, Experience with incrementality, Experience with scenario modeling, Experience with A/B testing, Experience with AI/ML solutions tradeoffs, Experience with production environments, Experience with product management teams, Experience with cross-functional leaders
What You'll Do.
Lead AI/ML capabilities strategy
Drive business decision-making
Lead high-impact problem spaces
Improve analytics product management practices
Understand player value
Create conditions for ML teams
Deliver production-grade products
Drive alignment across teams
Establish best practices
Influence portfolio-level decisions
Partner with Integrated Analytics Partners
Coordinate analytics investments
Enable product-driven analytics ecosystem
Lead discovery of AI/ML problem spaces
Coordinate across teams
Improve understanding of player value
Drive alignment across squads
Avoid duplication of effort
Identify opportunities to scale solutions
Standardize approaches
Lead product thinking across ML lifecycle
Own prioritization across multiple squads
Balance business impact
Balance technical maturity
Balance adoption potential
Balance resource constraints
Align work to business strategy
Shape analytics work sequencing
Balance new feature development
Balance operationalization
Balance productization
Align roadmaps for ML teams
Ensure statistical rigor
Ensure methodological consistency
Drive adoption of experimentation
Drive adoption of value-based analytical techniques
Partner with other Product Management teams
Operate as a unified team
Deliver cohesive strategies
Align Analytics strategy
Coordinate work across multiple squads
Deliver integrated analytics solutions
Influence stakeholders across functions
Drive thinking around scalability
Drive thinking around reuse
Drive thinking around sustainability
Improve understanding of player value
Partner with AI/ML Engineering
Transition capabilities into systems
Define success criteria for ML products
Ensure successful adoption of AI/ML capabilities
Embed analytics into business workflows
Embed ML into decision-making processes
Translate technical outputs into impact
Advocate for investments in shared capabilities
Engage with business stakeholders
Understand stakeholder needs
Gather stakeholder feedback
Clarify how ML outputs inform value
Support Integrated Analytics Partners
Translate strategic priorities
Communicate outcomes and impact
Support business understanding of value
Define best practices for analytics PM
Promote best practices
Mentor other Analytics Leads
Support other Analytics Leads
Identify gaps in work progression
Raise overall quality
Raise consistency of work
How You'll Work.
Team & Collaboration
Cross-functional leaders; Data Science leadership; Engineering leadership; Product Management teams; Integrated Analytics Partners
Communication Scope
Stakeholder communication; Executive presentations
Process & Methodology
Agile, Roadmap planning
Full Job 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, pr
Applying for this Data Science Product Manager role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Greenhouse
- Create a Greenhouse profile before applying — it saves time across multiple applications.
- Upload your resume as a PDF; the parser handles it better than Word.
- Answer all knockout questions carefully — wrong answers auto-reject before a human sees you.
- Enable email notifications to track application status in real time.
ANONYMOUS · UNFILTERED
What do employees actually say about PlayStation Global?
Real rants from real employees. Read before you apply.