Job Summary

The Applied Artificial Intelligence (AI) Scientist III is a subject matter expert, hands-on practitioner who designs, builds, and implements advanced artificial intelligence (AI) and machine learning (ML) solutions that directly enable the organization to execute its strategic priorities. This role combines deep technical expertise with healthcare domain knowledge to deliver scalable, production-grade AI applications that improve quality, reduce administrative waste, and enhance member outcomes.

The Applied AI Scientist III independently leads complex projects from ideation through operational deployment, working across data, technology, and business teams to develop models and algorithms that power key functions such as claims accuracy, care coordination, quality improvement, and fraud detection. This position is highly collaborative, frequently partnering with leaders across departments to understand business needs and translate them into AI-driven capabilities that deliver measurable value.

The Applied AI Scientist III serves as a mentor and role model for staff promoting best practices in model design, documentation, version control, and interpretability. The position is central to advancing the organization’s AI maturity—driving both innovation and execution within an applied, results-oriented framework. Acts as a Subject Matter Expert (SME), serves as a resource and mentor for other staff.

Duties

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.

Duties Continued

Education Required

Education Preferred

Experience

Required:

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.

Preferred:

Experience within a Managed Care Organization (MCO) or health plan environment (Medi-Cal, Medicare, or ACA Exchange).

Experience developing and operationalizing Large Language Models (LLM)-based solutions, including prompt engineering or retrieval-augmented generation (RAG).

Experience in risk adjustment, payment integrity, or quality measurement modeling.

Experience with healthcare data analytics and modeling in Managed Care settings.

Skills

Required:

Advanced programming skills in Python, including libraries for data processing, modeling, and analytics (e.g., Pandas, Scikit-learn, PySpark).

Deep understanding of machine learning and AI techniques, including supervised and unsupervised learning, feature engineering, model optimization, and explainability.

Strong analytical problem-solving skills with the ability to structure complex problems into actionable modeling tasks.

Exceptional written and verbal communication skills, including documentation and presentation of technical material to non-technical audiences.

Excellent collaboration skills and ability to lead cross-functional projects involving IT, business stakeholders, and analytics peers.

Excellent communication, documentation, and stakeholder engagement skills.

Preferred:

Knowledge of generative AI tools and frameworks (e.g., LangChain, OpenAI APIs, Azure OpenAI).

Knowledge and understanding of responsible AI principles, including bias detection, fairness, and explainability.

Knowledge of R or SQL for complementary analytics tasks.

Knowledge of Snowpark for scalable model deployment.

Knowledge of Shiny or Streamlit for AI-driven application delivery.

Licenses/Certifications Required

Licenses/Certifications Preferred

Required Training

Physical Requirements

Additional Information