Principal Data Scientist
Sedgwick
full-time
Posted on:
Location Type: Remote
Location: Idaho • Montana • United States
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Job Level
Tech Stack
About the role
- 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.
Requirements
- 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.
Benefits
- Health insurance
- Professional development opportunities
- Work-life balance
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
statistical modelingmachine learningpredictive analyticsgradient boostingdeep learningnatural language processinganomaly detectionprobabilistic modelingfeature engineeringmodel validation
Soft Skills
technical leadershipmentoringcommunicationcollaborationanalytical rigorproblem-solvinginnovationguidancetranslating findingsstakeholder engagement