Director Data Science

Sedgwick

1d•Remote

About The Position

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 Certified as a Great Place to Work® Fortune Best Workplaces in Financial Services & Insurance Director Data Science Job Responsibilities 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. Qualifications 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. #LI-TS1 #remote Sedgwick is an Equal Opportunity Employer and a Drug-Free Workplace. If you're excited about this role but your experience doesn't align perfectly with every qualification in the job description, consider applying for it anyway! Sedgwick is building a diverse, equitable, and inclusive workplace and recognizes that each person possesses a unique combination of skills, knowledge, and experience. You may be just the right candidate for this or other roles. Sedgwick is the world’s leading risk and claims administration partner, which helps clients thrive by navigating the unexpected. The company’s expertise, combined with the most advanced AI-enabled technology available, sets the standard for solutions in claims administration, loss adjusting, benefits administration, and product recall. With over 33,000 colleagues and 10,000 clients across 80 countries, Sedgwick provides unmatched perspective, caring that counts, and solutions for the rapidly changing and complex risk landscape. For more, see sedgwick.com

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.
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.

Nice To Haves

Experience in insurance, claims management, financial services, or healthcare analytics preferred.

Responsibilities

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.