Principal Data Scientist
Role details
Job location
Tech stack
Job description
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Lead the design and development of advanced statistical and machine learning models that improve claims outcomes, operational efficiency, and risk management.
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Serve as the technical authority for complex modeling initiatives including fraud detection, claims severity prediction, litigation risk modeling, and recovery optimization.
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Develop predictive and prescriptive models using structured and unstructured claims data, including adjuster notes, medical records, and policy documentation.
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Architect modeling approaches that leverage modern techniques such as gradient boosting, deep learning, NLP, anomaly detection, and probabilistic modeling.
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Partner with AI Engineering teams to productionize models and integrate them into enterprise AI platforms and operational systems.
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Design feature engineering strategies and modeling pipelines using large-scale enterprise datasets.
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Establish best practices for model development, experimentation, validation, and reproducibility.
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Lead advanced analytical techniques such as causal inference, scenario simulation, and risk scoring methodologies.
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Build and maintain model evaluation frameworks that measure accuracy, bias, stability, and business impact.
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Monitor deployed models for drift, degradation, and changing data distributions, and recommend recalibration strategies.
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Provide technical guidance to data scientists and analysts across the organization.
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Mentor junior team members on statistical methods, machine learning techniques, and analytical rigor.
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Translate complex analytical findings into clear, actionable insights for business leaders and operational teams.
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Collaborate with Claims Operations, Finance, Risk, and IT stakeholders to identify high-impact analytical opportunities.
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Evaluate external data sources and third-party analytical solutions that enhance predictive capabilities.
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Ensure analytical methodologies align with enterprise governance standards and regulatory expectations.
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Contribute to Sedgwick's broader AI and advanced analytics strategy by identifying emerging technologies and modeling approaches.
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Lead research and innovation initiatives that advance Sedgwick's predictive analytics capabilities.
Requirements
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Master's or PhD in Data Science, Statistics, Mathematics, Computer Science, Economics, or related quantitative discipline.
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8-12+ years of experience in data science, statistical modeling, or advanced analytics roles.
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Deep expertise in machine learning algorithms, statistical modeling techniques, and predictive analytics methodologies.
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Strong programming skills in Python, R, or similar analytical languages.
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Extensive experience working with large, complex datasets in enterprise environments.
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Proven experience designing and implementing end-to-end modeling pipelines.
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Strong understanding of model validation, feature engineering, and performance evaluation techniques.
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Experience collaborating with engineering teams to deploy models into production systems.
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Familiarity with distributed data processing tools and modern data platforms preferred.
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Experience in insurance, claims management, healthcare, or financial services analytics preferred.
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Ability to communicate advanced analytical concepts to both technical and non-technical stakeholders.
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Demonstrated ability to lead complex analytical initiatives that drive measurable business value.
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Strong mentoring and technical leadership capabilities.
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