Principal Data Scientist

Job 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.
• Prominent 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.