[Hiring] Director of Product Management, Data Science @Sedgwick

Role Description

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.

• Set product vision & roadmap
• 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.
• 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.
• Establishes outcomes that connect model performance to business results, and communicates product strategy, progress, risks, and architectural implications to executive stakeholders.
• Oversee, coordinate & support development work
• 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.
• Partners with Data Science, Data Engineering, and architecture leadership to manage dependencies, data pipelines, integrations, capacity planning, and delivery risks.
• Ensures a clear path from exploratory analysis and proof of concept to production.
• Owns accountability for portfolio KPIs and contributions to Product Group and enterprise OKRs.
• Portfolio governance & lifecycle management
• Maintains a holistic view of business processes, data sources, systems, and platform dependencies impacting the portfolio.
• Partners with data governance, privacy, legal, compliance, and model risk teams to ensure data science products meet regulatory requirements.
• Evaluates and approves significant product, model, process, and system changes.
• Acts as a senior escalation point for stakeholders, resolving conflicts related to priorities, dependencies, and delivery outcomes.
• Leads the full product and model lifecycle, ensuring traceability from strategic intent through realized business value.
• ADDITIONAL FUNCTIONS AND RESPONSIBILITIES
• Builds data literacy across business stakeholders.
• Stays current on advances in data science methods and practices.
• Performs other duties as assigned.
• Travel as required.

Qualifications

• Bachelor's degree in Statistics, Data Science, Mathematics, Computer Science, Economics, or a related quantitative field preferred.
• Advanced degree preferred.
• Ten (10) or more years of experience in product management or product strategy.
• At least three (3) years managing data science, predictive analytics, or machine learning products required.
• Demonstrated track record of taking predictive models or other data science solutions from concept through production deployment.
• Experience with agile development strongly preferred.
• Experience in claims, insurance, healthcare, financial services, or other regulated environments preferred.

Requirements

• Expert knowledge of agile methodologies and product operating models.
• Strong working knowledge of the data science lifecycle.
• Solid grounding in statistical concepts.
• Fluency in model evaluation measures.
• Understanding of experimental design.
• Familiarity with common data science tools and languages such as Python, R, and SQL.
• Working knowledge of model governance, model risk management, and data privacy requirements.
• Ability to translate business and portfolio strategy into product vision, roadmaps, and execution standards.
• Strong portfolio-level judgment.
• Advanced ability to lead complex, high-stakes discussions.
• Proven people leadership capability.

Benefits

• Work-life balance.
• Caring culture.
• Opportunities for career growth.