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Lead Data Scientist
Pareto Capital. Independently lead applied AI and data science workstreams translating healthcare and underwriting data into improvements in risk selection, pricing accuracy, operational efficiency, and underwriting experience .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in applied AI and data science, with a strong focus on healthcare and underwriting data to enhance risk selection and operational efficiency. Proficient in model development, validation, and deployment, ensuring compliance with responsible AI and regulatory standards.
Highest-signal resume keywords
Advanced PythonSQLMachine LearningMLOps LifecycleHealthcare Data Expertise
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data ScienceStatisticsPredictive ModelingFeature EngineeringStatistical DistributionsCalibrationOptimizationExplainabilitySupervised LearningUnsupervised Learning
Soft Skills
Business AcumenJudgmentCross-Functional CollaborationMentoring
Tools & Technologies
Scikit-learnXGBoostGBMsPySparkAWSGitKedroPyTorch
Industry Keywords
HealthcareInsurance RiskLongitudinal DataModel GovernanceResponsible AI
Tech Stack
Tools & technologiesAWSCloudPySparkPythonPyTorchScikit-LearnSQL
About the role
Key responsibilities & impact- Independently lead applied AI and data science workstreams translating healthcare and underwriting data into improvements in risk selection, pricing accuracy, operational efficiency, and underwriting experience
- Lead work from problem framing, target definition, feature engineering, and model development through validation, production deployment, monitoring, and business-impact measurement
- Manage workstream plans and risks, escalating major methodological, governance, or cross-platform decisions to the VP of AI and VP Analytics
- Build internal AI products and improve shared practices for temporal validation, explainability, responsible AI and data use, model governance, and performance monitoring
- Define and document analytics-ready datasets, data-quality requirements, and reusable features across claims, pharmacy, utilization, financial, underwriting, and external data
- Develop, compare, and challenge predictive models, distributions, and hybrid rule/model approaches
- Translate model needs into feature requirements and partner with business teams and Legal to secure approvals
- Manage third-party model evaluations and ROI analyses when needed
- Produce explainable, reproducible model outputs and follow documentation, testing, monitoring, retraining, and rollback standards
- Champion reuse, standardization, and componentization of data science assets across Pareto Predict
- Translate model outputs into decision support and recommend whether to scale, iterate, or stop based on measured business results
- Ensure solutions meet evaluation, monitoring, model-risk, responsible-AI, privacy, and regulatory expectations
- Contribute through technical reviews, reusable components, mentoring, and evaluation of new statistical, machine-learning, and AI methods
- Partner with Business, AI, Engineering, Underwriting, Product, and other stakeholders to deliver measurable outcomes
Requirements
What you’ll need- Bachelor's or master's degree in Statistics, Data Science, Computer Science, Mathematics, Engineering, or a related quantitative field; an advanced degree is a plus
- 8+ years in data science, machine learning, statistics, actuarial analytics, including substantial work with healthcare, pharmacy, insurance risk, or sensitive longitudinal data
- Proven record of independently owning models or analytical workstreams end to end
- Advanced Python and SQL
- Experience with scikit-learn, XGBoost, GBMs, or comparable frameworks
- Experience using PySpark or comparable distributed-computing tools is preferred
- PyTorch experience is a plus
- Deep expertise in supervised and unsupervised machine learning methods, explainability, statistical distributions, rare-event and high-cost modeling, calibration, and optimization
- Proven ownership of production models and the MLOps lifecycle on AWS or a comparable cloud platform
- Experience with Git-based version control, testing, deployment, monitoring, retraining, rollback, documentation, and responsible AI/model-governance practices
- Familiarity with Kedro or a similar pipeline framework is a plus
- Experience with LLMs, prompt engineering, RAG, embeddings, vector DB, or agentic frameworks is helpful but not required
- Strong business acumen and judgment, with ability to connect analytical outputs to measurable business outcomes and influence cross-functional decisions
- Must be authorized to work in the United States without sponsorship now or in the future
Benefits
Comp & perks- Fully paid medical, dental, and vision benefits
- Flexible PTO
- 401k company contribution
- Tuition reimbursement
- Professional development allowance
- Transportation allowance and daily parking reimbursement
- Engaging hybrid work environment