Lead Data Scientist
New
P
ParetoHealthHealthcare Insurance
Remote within the United StatesFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 8+ years in data science, machine learning, statistics, actuarial analytics
- Required Skills
- AWSPythonSQLMachine LearningData sciencescikit-learnMLOps
Requirements
- Bachelor's or master's degree in Statistics, Data Science, Computer Science, Mathematics, Engineering, or related quantitative field.
- 8+ years of experience in data science, machine learning, statistics, or actuarial analytics.
- Substantial experience with healthcare, pharmacy, insurance risk, or sensitive longitudinal data.
- Advanced proficiency in Python and SQL.
- Experience with frameworks such as scikit-learn, XGBoost, or GBMs.
- Proven experience owning production models and the MLOps lifecycle on AWS or a comparable cloud platform.
- Experience with Git-based version control, testing, deployment, and model monitoring.
- Deep expertise in supervised and unsupervised learning, explainability, rare-event modeling, and statistical distributions.
- Strong business acumen with the ability to connect analytical output to measurable business outcomes.
- Experience with PySpark or distributed-computing tools preferred.
- Experience with pipeline frameworks like Kedro is a plus.
Responsibilities
- Own end-to-end delivery and outcomes for predictive underwriting and pricing workstreams.
- Frame evidence-based recommendations and manage trade-offs across model quality, cost, and scalability.
- Define requirements for analytics-ready datasets, claims, pharmacy, and underwriting data.
- Develop, compare, and validate predictive models using rigorous point-in-time approaches.
- Champion reuse, standardization, and componentization of data science assets across the organization.
- Translate model outputs into decision support to improve underwriting accuracy and efficiency.
- Ensure solutions operate within regulatory, privacy, and responsible-AI expectations.
- Mentor team members and contribute to technical reviews and statistical method evaluations.
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