Data Scientist Principal, AI Development and Governance
J
JobgetherHealthcare Fraud Detection
United StatesFull-TimePrincipal
Salary119,000 - 161,000 USD per year
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Job Details
- Experience
- 8+ years
- Required Skills
- AWSPythonSQLMachine LearningSnowflakeGenerative AI
Requirements
- Master’s degree in statistics, computer science, engineering, applied mathematics, economics, or equivalent quantitative experience.
- 8+ years of experience building, validating, and deploying machine learning models using real-world data.
- Strong working knowledge of responsible AI and model-risk practices, including documentation, monitoring, and bias/drift detection.
- Demonstrated experience evaluating generative AI and LLM use cases for technical feasibility and risk.
- Strong Python and SQL skills with experience performing feature engineering in large-scale data warehouses.
- 2+ years of experience working with healthcare claims data (Medicare, Medicaid, or commercial).
- Working knowledge of medical terminology and coding systems such as ICD-10, CPT, HCPCS, and DRG.
- Experience presenting technical methodologies and governance decisions to stakeholders.
- Strong analytical and critical-thinking skills.
- Experience with graph or network analytics or entity resolution (advantage).
- Experience with AWS and/or Snowflake environments (beneficial).
Responsibilities
- Establish modeling and validation standards for the Data Science team, including expectations for documentation, monitoring, bias assessment, and production readiness.
- Review data science models against established standards before production deployment and provide recommendations for quality and governance improvements.
- Develop and maintain responsible-AI and generative-AI policies for customer-facing and internal tools.
- Build and deploy machine learning models for healthcare fraud, waste, and abuse detection, including supervised risk scoring and feature engineering on claims data.
- Validate models under significant class imbalance and evolving fraud patterns.
- Evaluate generative AI applications for feasibility and risk within a highly scrutinized healthcare environment.
- Explain model methodologies, governance controls, and AI-assisted processes to partners, internal stakeholders, and auditors.
- Defend technical and governance decisions to both technical and non-technical audiences.
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