• Define and lead Sedgwick’s enterprise data science strategy aligned to claims optimization, risk management, fraud detection, and client performance analytics. • Build and scale a high-performing team of data scientists, quantitative analysts, and ML practitioners supporting global operations. • Drive development of predictive and prescriptive models for claims severity, reserving, subrogation, litigation risk, recovery optimization, and fraud detection. • Oversee statistical modeling, machine learning, and advanced analytics initiatives from ideation through production deployment. • Partner with AI Engineering to transition research models into scalable, production-grade systems. • Establish modeling standards, validation protocols, and reproducibility requirements across the organization. • Lead experimentation frameworks including A/B testing, causal inference analysis, and performance measurement methodologies. • Ensure model explainability, transparency, and fairness for analytics that influence claim decisions or financial outcomes. • Collaborate with Claims Operations, Finance, Actuarial, and IT teams to identify high-value analytical opportunities. • Guide development of feature engineering strategies using structured and unstructured claims data. • Oversee creation of enterprise data assets, analytical datasets, and model-ready pipelines in partnership with data engineering. • Implement governance processes for model validation, drift monitoring, recalibration, and lifecycle management. • Provide thought leadership in advanced analytics including time-series forecasting, anomaly detection, NLP, and risk scoring. • Translate complex analytical findings into actionable business insights for senior leadership. • Develop KPI frameworks to measure operational improvements driven by analytics initiatives. • Ensure compliance with regulatory requirements and internal data governance standards. • Evaluate external data sources and analytics partnerships that enhance predictive capabilities. • Manage budgets, vendor relationships, and analytical tooling investments. • Present data-driven insights and modeling outcomes to executive leadership and client stakeholders. • Foster a culture of analytical rigor, innovation, and continuous improvement.