Principal Data Scientist (Sioux Falls)

Overview

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

Principal Data Scientist

Sedgwick is an Equal Opportunity Employer and a Drug-Free Workplace.

#LI-TS1 #remote

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

Responsibilities

• Lead the design and development of advanced statistical and machine learning models that improve claims outcomes, operational efficiency, and risk management.
• Serve as the technical authority for complex modeling initiatives including fraud detection, claims severity prediction, litigation risk modeling, and recovery optimization.
• Develop predictive and prescriptive models using structured and unstructured claims data, including adjuster notes, medical records, and policy documentation.
• Architect modeling approaches that leverage modern techniques such as gradient boosting, deep learning, NLP, anomaly detection, and probabilistic modeling.
• Partner with AI Engineering teams to productionize models and integrate them into enterprise AI platforms and operational systems.
• Design feature engineering strategies and modeling pipelines using large-scale enterprise datasets.
• Establish best practices for model development, experimentation, validation, and reproducibility.
• Lead advanced analytical techniques such as causal inference, scenario simulation, and risk scoring methodologies.
• Build and maintain model evaluation frameworks that measure accuracy, bias, stability, and business impact.
• Monitor deployed models for drift, degradation, and changing data distributions, and recommend recalibration strategies.
• Provide technical guidance to data scientists and analysts across the organization.
• Mentor junior team members on statistical methods, machine learning techniques, and analytical rigor.
• Translate complex analytical findings into clear, actionable insights for business leaders and operational teams.
• Collaborate with Claims Operations, Finance, Risk, and IT stakeholders to identify high-impact analytical opportunities.
• Evaluate external data sources and third-party analytical solutions that enhance predictive capabilities.
• Ensure analytical methodologies align with enterprise governance standards and regulatory expectations.
• Contribute to Sedgwick’s broader AI and advanced analytics strategy by identifying emerging technologies and modeling approaches.
• Lead research and innovation initiatives that advance Sedgwick’s predictive analytics capabilities.

Qualifications

• Master’s or PhD in Data Science, Statistics, Mathematics, Computer Science, Economics, or related quantitative discipline.
• 8–12+ years of experience in data science, statistical modeling, or advanced analytics roles.
• Deep expertise in machine learning algorithms, statistical modeling techniques, and predictive analytics methodologies.
• Strong programming skills in Python, R, or similar analytical languages.
• Extensive experience working with large, complex datasets in enterprise environments.
• Proven experience designing and implementing end-to-end modeling pipelines.
• Strong understanding of model validation, feature engineering, and performance evaluation techniques.
• Experience collaborating with engineering teams to deploy models into production systems.
• Familiarity with distributed data processing tools and modern data platforms preferred.
• Experience in insurance, claims management, healthcare, or financial services analytics preferred.
• Ability to communicate advanced analytical concepts to both technical and non-technical stakeholders.
• Demonstrated ability to lead complex analytical initiatives that drive measurable business value.
• Strong mentoring and technical leadership capabilities.