Director of Product Management, Data Science

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The Director of Product Management, Data Science defines and executes product strategy and outcomes for a portfolio of data science products, including predictive models, statistical and forecasting solutions, and advanced analytics capabilities. This role owns roadmap direction, investment prioritization, and delivery accountability, ensuring data science work moves beyond analysis and experimentation into scalable, production-ready solutions that deliver measurable business value. The Director leads Product Managers, partners closely with Data Science, Data Engineering, and architecture leadership, and ensures data science products are accurate, well-governed, and aligned with enterprise objectives.

• Defines and owns the product vision and multi-year roadmap for a portfolio of data science products, aligning business priorities with data platform direction, architectural standards, and long-term scalability.
• Leads portfolio discovery and strategic planning to identify high-value opportunities for predictive modeling, forecasting, segmentation, and optimization, assessing business impact, data availability and quality, technical feasibility, and investment considerations before committing to build.
• Ensures business problems are framed in ways data science can solve, with clear decision points, success measures, and an understanding of how model outputs will be used in operational workflows.
• Partners with senior business, technology, data science, and operations leaders to align scope, sequencing, and investment decisions, ensuring shared understanding of constraints, data dependencies, and model limitations.
• Establishes outcomes that connect model performance to business results, and communicates product strategy, progress, risks, and architectural implications to executive stakeholders.

• Oversees delivery across product and data science teams, ensuring alignment between product strategy, model development, technical execution, and client outcomes.
• Sets expectations for backlog quality and acceptance criteria across teams, including standards for model performance thresholds, validation methods, interpretability, and data requirements.
• Partners with Data Science, Data Engineering, and architecture leadership to manage dependencies, data pipelines, integrations, capacity planning, and delivery risks across the portfolio.
• Ensures a clear path from exploratory analysis and proof of concept to production, including defined criteria for advancing, scaling, or retiring models.
• Owns accountability for portfolio KPIs and contributions to Product Group and enterprise OKRs, including model accuracy and stability, adoption, business value realized, quality, efficiency, and sustainability.

• Maintains a holistic view of business processes, data sources, systems, and platform dependencies impacting the portfolio and informs strategic and delivery decisions accordingly.
• Partners with data governance, privacy, legal, compliance, and model risk teams to ensure data science products meet regulatory requirements…