Data Scientist

Sedgwick is a global leader in risk and claims management, dedicated to helping clients navigate the complexities of the unexpected. With a workforce of over 33,000 professionals and serving more than 10,000 clients across 80 countries, Sedgwick provides innovative solutions in claims administration, loss adjusting, benefits management, and product recall. Recognized for its exceptional workplace culture, Sedgwick has been named one of America's Greatest Workplaces by Newsweek, certified as a Great Place to Work®, and listed among Fortune's Best Workplaces in Financial Services & Insurance.

About the role

As a Principal Data Scientist at Sedgwick, you will play a pivotal role in leading the development of sophisticated statistical and machine learning models aimed at enhancing claims outcomes, operational efficiency, and risk management strategies. You will serve as the technical authority on complex modeling initiatives, including fraud detection, claims severity prediction, litigation risk modeling, and recovery optimization. Your expertise will drive the design of innovative predictive and prescriptive models utilizing structured and unstructured data sources such as medical records, adjuster notes, and policy documents. Collaborating closely with AI engineering teams, you will ensure seamless deployment of models into production environments and integrate them into enterprise AI platforms. Your leadership will foster best practices in model development, validation, and experimentation, while mentoring junior team members and translating analytical insights into actionable business strategies.

Responsibilities

• Lead the design and development of advanced statistical and machine learning models to improve claims processing and risk management
• Serve as the technical expert for complex modeling initiatives, including fraud detection, claims severity prediction, and litigation risk assessment
• Develop predictive and prescriptive models using structured and unstructured data sources
• Architect innovative modeling approaches leveraging techniques such as gradient boosting, deep learning, NLP, anomaly detection, and probabilistic modeling
• Partner with AI engineering teams to productionize models and ensure seamless integration into operational systems
• Design feature engineering strategies and build scalable modeling pipelines utilizing large enterprise datasets
• Establish best practices for model experimentation, validation, and reproducibility
• Implement advanced analytical techniques including causal inference, scenario simulation, and risk scoring
• Develop and maintain model evaluation frameworks to monitor accuracy, bias, stability, and business impact
• Monitor deployed models for data drift and performance degradation, recommending recalibration as needed
• Provide technical guidance and mentorship to data scientists and analysts across the organization
• Translate complex analytical insights into clear, actionable recommendations for business and operational teams
• Collaborate with cross-functional teams including Claims Operations, Finance, Risk, and IT to identify analytical opportunities
• Evaluate external data sources and third-party solutions to enhance predictive capabilities
• Ensure all analytical methodologies comply with enterprise governance standards and regulatory requirements
• Contribute to Sedgwick’s broader AI and analytics strategy by exploring emerging technologies and innovative modeling approaches
• Lead research initiatives to advance the company's predictive analytics capabilities and stay ahead of industry trends

Requirements

• Master’s or PhD in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative discipline
• 8–12+ years of experience in data science, statistical modeling, or advanced analytics roles
• Deep expertise in machine learning algorithms, statistical techniques, and predictive modeling methodologies
• Proficiency in programming languages such as Python, R, or similar analytical tools
• Extensive experience working with large, complex datasets within enterprise environments
• Proven ability to design and implement end-to-end modeling pipelines
• Strong understanding of model validation, feature engineering, and performance evaluation techniques
• Experience collaborating with engineering teams for model deployment into production systems
• Familiarity with distributed data processing tools and modern data platforms is preferred
• Experience in insurance, claims management, healthcare, or financial services analytics is advantageous
• Excellent communication skills to convey complex analytical concepts to diverse stakeholders
• Demonstrated leadership in managing complex analytical projects that deliver measurable business value
• Strong mentoring skills and the ability to foster a collaborative team environment

Benefits

• Competitive salary and performance-based incentives
• Comprehensive health, dental, and vision insurance plans
• Retirement savings plans with company matching contributions
• Paid time off and holidays to support work-life balance
• Opportunities for professional development and continuous learning
• Flexible work arrangements, including remote work options
• Employee wellness programs and resources
• Inclusive and diverse workplace culture that values innovation and collaboration