Director of Product Management, Data Science

About the role

Sedgwick is hiring a Director of Product Management, Data Science, a telecommuter position. The Director defines and executes product strategy and outcomes for a portfolio of data science products, including predictive models, statistical and forecasting solutions and advanced analytics. The role owns roadmap direction, investment prioritization and delivery accountability, and leads Product Managers while partnering with Data Science, Data Engineering and architecture leadership.

Key responsibilities

• Set product vision and multi-year roadmap, and lead portfolio discovery and strategic planning for predictive modeling, forecasting, segmentation and optimization
• Frame business problems so they can be solved with data science, and communicate strategy, progress and risks to executives
• Oversee delivery across product and data science teams, set backlog quality and acceptance criteria, and manage dependencies, pipelines and delivery risks
• Own portfolio KPIs and contributions to Product Group and enterprise OKRs, including model accuracy, stability, adoption and business value
• Manage the full product and model lifecycle, from proof of concept to production, including post-deployment monitoring, recalibration and retirement
• Partner with data governance, privacy, legal, compliance and model risk teams, and approve major product and model changes
• Build data literacy among business stakeholders and stay current on advances in data science methods

Required profile

• Bachelor's degree in Statistics, Data Science, Mathematics, Computer Science, Economics or a related quantitative field (advanced degree preferred)
• 10+ years in product management or product strategy, including at least 3 years managing data science, predictive analytics or machine learning products
• Track record of moving predictive models from concept to production with measurable business impact
• Experience in claims, insurance, healthcare, financial services or other regulated environments preferred

Required skills

• Expert knowledge of agile methodologies and product operating models
• Strong working knowledge of the data science lifecycle and statistical concepts (regression, classification, forecasting, sampling)
• Fluency in model evaluation measures (precision, recall, AUC, calibration, error rates) and understanding of A/B testing and experimental design
• Familiarity with Python, R and SQL, and working knowledge of model governance, model risk and data privacy
• People leadership capability, including developing Product Managers and building high-performing teams