Staff Data Science Product Manager

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