Data Science Product Manager

- Bachelor’s degree in Business, Data Science, Computer Science, or a related field. - 12+ years of relevant experience, including 6+ years in digital 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, and productionization. - Demonstrated ability to operate in 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 communication and stakeholder management skills, including working with senior leaders. - Strong mentorship skills and experience elevating the capabilities of other product managers or analytics leaders. - Lead discovery and definition of ambiguous, high-impact AI/ML problem spaces. - 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. - 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. - 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. - Partner closely with other Product Management teams and cross-functional leaders. - Align Analytics strategy, prioritization, and execution through collaboration with 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. - Drive thinking around scalability, reuse, and long-term sustainability of analytics solutions. - 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. - Ensure successful adoption of AI/ML capabilities by end users. - Ensure analytics and ML capabilities are embedded into business workflows and decision-making processes. - Advocate for investments in shared capabilities and platforms when beneficial. - Engage with business stakeholders to understand needs, gather feedback, communicate progress. - Support Integrated Analytics Partners in translating strategic priorities into actionable work. - Communicate outcomes and impact of analytics initiatives clearly and effectively. - Define and promote best practices for analytics product management. - Mentor and support other Analytics Experimentation & Product Leads. - Identify gaps 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.