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Lead Data Scientist

3 hours, 12 minutes ago
United States
Full-time
Lead
Data Scientist
Data Science and Analytics
Apache Spark AWS Deep Learning Feature Engineering Generative AI Git MLOps Python PyTorch Scikit-learn SQL XGBoost
Apply Now
ParetoHealth

ParetoHealth

ParetoHealth revolutionizes employee health benefits with the largest captive in the nation, reducing costs, capping risks, and empowering businesses to control healthcare spending.

Insurance
51-250
Founded 2011
View All Jobs 5

Description

  • Own end-to-end predictive underwriting and pricing workstreams from problem framing and feature engineering through deployment, monitoring, retraining, and impact measurement.
  • Define analytics-ready datasets, data-quality standards, and reusable features across claims, pharmacy, utilization, financial, underwriting, and external data.
  • Apply point-in-time development and out-of-time validation to address claims maturity, seasonality, leakage, stability, and uncertainty.
  • Develop, compare, and challenge predictive models, statistical distributions, and hybrid rule/model approaches.
  • Evaluate emerging methods, including deep learning and GenAI, when they can improve accuracy, scalability, decision quality, or efficiency.
  • Translate model requirements into features, collaborate with business and Legal teams, and manage third-party evaluations and ROI analyses when needed.
  • Produce explainable, reproducible model outputs while following documentation, testing, monitoring, retraining, rollback, responsible-AI, privacy, and governance standards.
  • Build reusable data science components, pipelines, evaluation patterns, and scoring capabilities across Pareto Predict.
  • Translate model outputs into underwriting decision support and recommend whether solutions should scale, iterate, or stop based on measured results.
  • Provide technical reviews, mentoring, reusable components, and improvements to shared data science practices.

Requirements

  • Bachelor’s or master’s degree in Statistics, Data Science, Computer Science, Mathematics, Engineering, or a related quantitative field; advanced degree preferred.
  • 8+ years of experience in data science, machine learning, statistics, or actuarial analytics, including substantial healthcare, pharmacy, insurance-risk, or sensitive longitudinal data experience.
  • Proven ability to independently own models or analytical workstreams from development through production.
  • Advanced Python and SQL, with experience in scikit-learn, XGBoost, GBMs, or comparable frameworks.
  • Expertise in supervised and unsupervised machine learning, explainability, statistical distributions, rare-event and high-cost modeling, calibration, and optimization.
  • Production model and MLOps lifecycle experience on AWS or a comparable cloud platform, including Git, testing, deployment, monitoring, retraining, rollback, documentation, and governance.
  • PySpark or comparable distributed-computing experience preferred; PyTorch experience is a plus.
  • Kedro or similar pipeline framework experience preferred; familiarity with LLMs, prompt engineering, RAG, embeddings, vector databases, or agentic frameworks is helpful.
  • Strong business judgment and communication skills, with the ability to connect analytical work to measurable outcomes and influence cross-functional decisions.
  • Must be authorized to work in the United States without employment visa sponsorship now or in the future.

Benefits

  • Fully paid medical, dental, and vision benefits.
  • Flexible PTO.
  • 401(k) company contribution.
  • Tuition reimbursement.
  • Professional development allowance.
  • Transportation allowance and daily parking reimbursement.
  • Hybrid work environment.

Interested in this position?

Apply directly on the company website

Apply Now

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