Design, train, validate, and deploy complex AI and ML models to address enterprise use cases across departments such as Health Services, Payment Integrity, Quality Improvement, Finance, and Provider Network Management
Lead all phases of the AI solution lifecycle – from problem framing and data engineering through model design, validation, and operational integration
Implement production-grade ML pipelines using modern MLOps practices, ensuring scalability, reproducibility, and continuous model performance monitoring
Serve as a subject matter expert in responsible and explainable AI, ensuring model fairness, transparency, and compliance with regulatory and ethical standards
Partner with business and technology leaders to identify and prioritize new AI use cases that align with the organization’s transformation strategy
Translate business challenges into well-structured analytical problems and lead cross-functional teams through data discovery, feature engineering, and algorithm development
Work directly with cloud-based data and AI platforms (e.g., Snowflake, Azure ML, Databricks) to operationalize model delivery and integration with enterprise data assets
Mentor and coach staff, providing technical guidance, code reviews, and knowledge sharing
Document all model design assumptions, data sources, evaluation metrics, and deployment protocols for transparency and reproducibility
Communicate complex technical results in accessible, actionable ways for both executive and operational stakeholders
Contribute to the development of reusable AI assets, libraries, and standardized templates to accelerate future model development
Remain current on emerging AI/ML technologies, frameworks, and healthcare analytics applications, and advise leadership on adoption opportunities
Apply subject matter expertise in evaluating business operations and processes
Identify areas where technical solutions would improve business performance
Consult across business operations, provide mentorship, and contribute specialized knowledge
Ensure that the facts and details are correct so that the program's deliverable meets the needs of the department, organization and legislation's policies, standards, and best practices
Provide training, recommend process improvements, and mentor staff, department interns, etc. as needed
Perform other duties as assigned

Requirements

Master's Degree
At least 6 years of professional experience developing and deploying machine learning and AI solutions in enterprise or healthcare environments
Demonstrated experience leading full AI solution lifecycles – from problem definition to deployment and monitoring
Proven successful experience developing predictive models using structured and unstructured healthcare data (e.g., claims, encounters, eligibility, provider, quality metrics)
Experience with Python (Pandas, Scikit-learn, PySpark), distributed data frameworks (Spark), and MLOps concepts
Strong collaboration and mentorship experience, including guiding junior data scientists and analysts
Experience integrating AI solutions into production environments in collaboration with IT or Data Engineering
Experience with version control (Git) and model documentation best practices
Experience building and deploying models in production using MLOps frameworks and cloud platforms

Benefits

Paid Time Off (PTO)
Tuition Reimbursement
Retirement Plans
Medical, Dental and Vision
Wellness Program
Volunteer Time Off (VTO)

Applicant Tracking System Keywords

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

Hard skills

machine learningAI solutionspredictive modelsdata engineeringfeature engineeringMLOpsmodel validationmodel deploymentalgorithm developmentcloud-based data platforms

Soft skills

mentorshipcollaborationcommunicationproblem framingtechnical guidanceknowledge sharingleadershipanalytical thinkingtransparencyprocess improvement

Certifications

Master's Degree