Director, Data Science - Product Management

PlayStation Global•San Mateo, CA

7d•$273,000 - $409,600•Hybrid

About The Position

At Sony Interactive Entertainment, PlayStation creates unforgettable gaming experiences for millions of players around the world. As a global leader in interactive entertainment, we operate at massive scale across a complex digital ecosystem spanning hardware, software, services, commerce, marketing, and player engagement. We are seeking a Director of Data Science Product Management to lead the strategy, prioritization, and execution of data science to drive better decision-making and player experiences across PlayStation. This role is responsible for portfolio of products across the data science lifecycle - identifying high-value analytical opportunities, shaping predictive and decision products, and ensuring they deliver measurable business outcomes through adoption, monitoring and continuous improvement. The role will lead a team of Data Science Product Managers responsible for applying data science to solve business problems, including capabilities across customer lifetime value, player lifecycle intelligence, forecasting, simulations, audience intelligence and decision automation. The Director will help ensure that SIE’s data science investments are focused on the highest-impact opportunities, built with strong product discipline, and adopted in the workflows where they can create measurable business value.

Requirements

Bachelor’s degree in Business, Data Science, Computer Science, Statistics, Economics, Engineering, or a related field.
15+ years of relevant experience, including 8+ years in digital product management, data product management, AI/ML product management, analytics product management, or related roles.
6+ years of people management experience, including experience leading Product Managers or senior product-oriented analytics leaders.
Proven experience leading complex, cross-functional portfolios involving Data Science, ML Engineering, Analytics, Data Engineering, and business stakeholders.
Strong understanding of data science and machine learning workflows, including experimentation, predictive modeling, forecasting, segmentation, recommender systems, lifecycle modeling, productionization, and monitoring.
Experience with products or capabilities related to CLV/LTV, personalization, churn, propensity modeling, forecasting, audience targeting, marketing optimization, commerce, or digital engagement.
Familiarity with modern data and ML ecosystems such as SQL, Python, Snowflake, Spark, feature stores, MLOps tools, model monitoring, APIs, and analytics platforms.
Demonstrated ability to translate ambiguous business needs into product strategies, roadmaps, priorities, and measurable outcomes.
Strong executive communication skills, including the ability to translate technical concepts into business impact.
Experience leading, mentoring, and developing high-performing product teams.

Nice To Haves

Experience in gaming, digital commerce, subscription, marketplace, streaming, or consumer technology businesses.
Deep experience with customer intelligence, player lifecycle analytics, personalization, experimentation, audience intelligence, or growth optimization.
Experience defining success metrics and quality standards for data science products, including adoption, reliability, latency, interpretability, fairness, model drift, and business impact.
Experience building product management capabilities, teams, or operating models from the ground up.
Demonstrated ability to use AI-enabled tools, including generative or agentic AI, to improve product workflows, accelerate insight generation, and enhance decision-making.

Responsibilities

Translate business priorities into structured problem statements, investment cases, product strategies, and measurable outcomes, ensuring data science work is managed as a scalable product portfolio rather than one-off analytical requests.
Drive product strategy and lifecycle ownership for player value and decision intelligence capabilities, such as CLV, engagement and forecasting, ensuring high quality products are advanced from discovery through productionization, adoption, monitoring, iteration, and long-term value realization.
Establish clear criteria for when analytical work should remain exploratory, become a recurring decision process, or graduate into a production-grade product.
Define success metrics and quality standards for data science products, including business impact, adoption, reliability, interpretability, model quality, operational sustainability, and decision quality.
Partner with Data Science and Analytics leaders to ensure rigor across experimentation, causal inference, forecasting, incrementality measurement, model evaluation, and value analysis, while promoting these approaches as core decision-making tools.
Influence senior stakeholders across functions to align strategy, clarify priorities, manage tradeoffs, and drive adoption of data science products.
Guide Product Managers across the product lifecycle while directly leading or sponsoring the organization's highest-impact and most ambiguous product initiatives.
Create a strong Data Science product culture centered on hypothesis-driven discovery, evidence-based prioritization, measurable business impact and close partnership with cross-functional partners.