Director, Data Science

Role Overview

• 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.

Requirements

• Master’s or PhD in Data Science, Statistics, Mathematics, Computer Science, Economics, or related quantitative field.
• 10+ years of experience in data science, advanced analytics, or quantitative modeling.
• 5+ years of leadership experience managing data science or analytics teams.
• Deep expertise in statistical modeling, machine learning, and predictive analytics.
• Strong programming skills in Python, R, or similar analytical languages.
• Experience deploying models into production environments in collaboration with engineering teams.
• Strong understanding of feature engineering, model validation, and performance evaluation techniques.
• Experience working with large, complex datasets in enterprise data environments.
• Knowledge of data governance, regulatory compliance, and model risk management practices.
• Experience in insurance, claims management, financial services, or healthcare analytics preferred.
• Ability to communicate technical concepts and analytical insights to non-technical stakeholders.
• Strong strategic thinking skills with the ability to align analytics initiatives to measurable business outcomes.
• Demonstrated success delivering analytics solutions that drive operational efficiency and financial impact.

Tech Stack

• Python

Benefits

• Health insurance
• Retirement plans
• Paid time off
• Flexible work arrangements
• Professional development