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R79086

By joining Sedgwick, you'll be part of something truly meaningful. It's what our 33,000 colleagues do every day for people around the world who are facing the unexpected. We invite you to grow your career with us, experience our caring culture, and enjoy work-life balance. Here, there's no limit to what you can achieve.

Newsweek Recognizes Sedgwick as America's Greatest Workplaces National Top Companies

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Fortune Best Workplaces in Financial Services & Insurance

Director of Product Management, Data Science

PRIMARY PURPOSE:

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.

ESSENTIAL FUNCTIONS AND RESPONSIBILITIES

_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, 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.

_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, 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.

_Portfolio governance & lifecycle management_

+ 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 and model governance standards, including documentation, validation, fairness, and interpretability.

+ Evaluates and approves significant product, model, process, and system changes, assessing impact, risk, technical health, and value tradeoffs.

+ Acts as a senior escalation point for stakeholders, resolving conflicts related to priorities, dependencies, and delivery outcomes.

+ Leads the full product and model lifecycle, including release readiness, post-deployment monitoring for performance and drift, recalibration and retirement decisions, and continuous improvement, ensuring traceability from strategic intent through realized business value.

ADDITIONAL FUNCTIONS AND RESPONSIBILITIES

+ Builds data literacy across business stakeholders, helping leaders interpret model outputs, understand uncertainty, and set realistic expectations for outcomes and timelines.

+ Stays current on advances in data science methods and practices, and assesses their relevance to the portfolio.

+ Performs other duties as assigned.

+ Travel as required.

QUALIFICATIONS

Education & Licensing

Bachelor's degree in Statistics, Data Science, Mathematics, Computer Science, Economics, or a related quantitative field from an accredited college or university preferred. Advanced degree preferred. Licenses as needed.

Experience

+ Ten (10) or more years of experience in product management or product strategy, including 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 and measurable business impact required.

+ Experience with agile development strongly preferred.

+ Experience in claims, insurance, healthcare, financial services, or other regulated environments preferred.

Skills & Knowledge

+ Expert knowledge of agile methodologies and product operating models, including scaling delivery across multiple teams and products.

+ Strong working knowledge of the data science lifecycle, including problem framing, data preparation, feature development, model development, validation, deployment, monitoring, and recalibration.

+ Solid grounding in statistical concepts, including regression, classification, forecasting, sampling, and statistical significance.

+ Fluency in model evaluation measures (e.g., precision, recall, AUC, calibration, error rates) and the ability to translate model performance into business impact.

+ Understanding of experimental design, including A/B testing, control groups, and pilot design, to validate model value before scaling.

+ Familiarity with common data s...

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