Sedgwick

Director, Data Science

Sedgwick

full-time

Posted on:

Location Type: Remote

Location: Idaho • Montana • United States

Visit company website

Explore more

✨ AI Apply
Apply

Job Level

Lead

Tech Stack

Python

About the role

  • Define and lead Sedgwick’s enterprise data science strategy aligned to claims optimization, risk management, fraud detection, and client performance analytics.
  • Build and scale a high-performing team of data scientists, quantitative analysts, and ML practitioners supporting global operations.
  • Drive development of predictive and prescriptive models for claims severity, reserving, subrogation, litigation risk, recovery optimization, and fraud detection.
  • Oversee statistical modeling, machine learning, and advanced analytics initiatives from ideation through production deployment.
  • Partner with AI Engineering to transition research models into scalable, production-grade systems.
  • Establish modeling standards, validation protocols, and reproducibility requirements across the organization.
  • Lead experimentation frameworks including A/B testing, causal inference analysis, and performance measurement methodologies.
  • Ensure model explainability, transparency, and fairness for analytics that influence claim decisions or financial outcomes.
  • Collaborate with Claims Operations, Finance, Actuarial, and IT teams to identify high-value analytical opportunities.
  • Guide development of feature engineering strategies using structured and unstructured claims data.
  • Oversee creation of enterprise data assets, analytical datasets, and model-ready pipelines in partnership with data engineering.
  • Implement governance processes for model validation, drift monitoring, recalibration, and lifecycle management.
  • Provide thought leadership in advanced analytics including time-series forecasting, anomaly detection, NLP, and risk scoring.
  • Translate complex analytical findings into actionable business insights for senior leadership.
  • Develop KPI frameworks to measure operational improvements driven by analytics initiatives.
  • Ensure compliance with regulatory requirements and internal data governance standards.
  • Evaluate external data sources and analytics partnerships that enhance predictive capabilities.
  • Manage budgets, vendor relationships, and analytical tooling investments.
  • Present data-driven insights and modeling outcomes to executive leadership and client stakeholders.
  • Foster a culture of analytical rigor, innovation, and continuous improvement.

Requirements

  • Master’s or PhD in Data Science, Statistics, Mathematics, Computer Science, Economics, or related quantitative field.
  • 10+ years of experience in data science, advanced analytics, or quantitative modeling.
  • 5+ years of leadership experience managing data science or analytics teams.
  • Deep expertise in statistical modeling, machine learning, and predictive analytics.
  • Strong programming skills in Python, R, or similar analytical languages.
  • Experience deploying models into production environments in collaboration with engineering teams.
  • Strong understanding of feature engineering, model validation, and performance evaluation techniques.
  • Experience working with large, complex datasets in enterprise data environments.
  • Knowledge of data governance, regulatory compliance, and model risk management practices.
  • Experience in insurance, claims management, financial services, or healthcare analytics preferred.
  • Ability to communicate technical concepts and analytical insights to non-technical stakeholders.
  • Strong strategic thinking skills with the ability to align analytics initiatives to measurable business outcomes.
  • Demonstrated success delivering analytics solutions that drive operational efficiency and financial impact.
Benefits
  • Health insurance
  • Retirement plans
  • Paid time off
  • Flexible work arrangements
  • Professional development
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
data sciencestatistical modelingmachine learningpredictive analyticsfeature engineeringmodel validationperformance evaluationtime-series forecastinganomaly detectionnatural language processing
Soft Skills
leadershipstrategic thinkingcommunicationcollaborationanalytical rigorinnovationcontinuous improvementpresentation skillsproblem-solvingclient engagement
Certifications
Master’s in Data SciencePhD in Data SciencePhD in StatisticsPhD in MathematicsPhD in Computer SciencePhD in Economics