Applied Artificial Intelligence Scientist III

L.A. Care Health Plan•Los Angeles, CA

2d•$135,136 - $216,218

About The Position

The Applied Artificial Intelligence (AI) Scientist III is a hands-on subject matter expert who designs, builds, and implements advanced AI and machine learning solutions that support L.A. Care Health Plan’s strategic priorities. The role combines technical expertise with healthcare domain knowledge to deliver scalable, production-grade applications that improve quality, reduce administrative waste, and enhance member outcomes. The position independently leads complex projects from ideation through operational deployment, collaborating across data, technology, and business teams. It develops models and algorithms for functions such as claims accuracy, care coordination, quality improvement, and fraud detection, and works with leaders across departments to translate business needs into measurable AI-driven capabilities. The Applied AI Scientist III mentors staff and promotes best practices in model design, documentation, version control, and interpretability. The role advances organizational AI maturity through innovation and execution in an applied, results-oriented framework.

Requirements

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 through deployment and monitoring.
Successful experience developing predictive models using structured and unstructured healthcare data, such as claims, encounters, eligibility, provider, and quality metrics.
Experience with Python, including Pandas, Scikit-learn, and PySpark; distributed data frameworks such as Spark; and MLOps concepts.
Strong collaboration and mentorship experience, including guiding junior data scientists and analysts.
Experience integrating AI solutions into production in collaboration with IT or Data Engineering.
Experience with Git version control and model documentation best practices.
Experience building and deploying production models using MLOps frameworks and cloud platforms.
Advanced Python programming skills using libraries for data processing, modeling, and analytics.
Deep understanding of machine learning and AI techniques, including supervised and unsupervised learning, feature engineering, model optimization, and explainability.
Strong analytical problem-solving skills and ability to structure complex problems into actionable modeling tasks.
Exceptional written and verbal communication skills, including documenting and presenting 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.
Master’s degree; equivalent education and/or experience may be considered in lieu of a degree.

Nice To Haves

Doctorate degree.
Experience within a Managed Care Organization or health plan, including Medi-Cal, Medicare, or ACA Exchange.
Experience developing and operationalizing LLM-based solutions, including prompt engineering or retrieval-augmented generation.
Experience in risk adjustment, payment integrity, or quality measurement modeling.
Experience with healthcare data analytics and modeling in Managed Care settings.
Knowledge of generative AI tools and frameworks such as LangChain, OpenAI APIs, or 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.
Preferred certifications: Snowflake SnowPro Core Certification; SnowPro Specialty: Snowpark Certification; Python Institute PCEP or PCAP; HarvardX or Johns Hopkins Data Science Certificate (R); Microsoft Certified: Data Scientist Associate (DP-100); Certified Analytics Professional (CAP); Certified Health Data Analyst (CHDA); Microsoft Certified Professional (MCP).

Responsibilities

Design, train, validate, and deploy complex AI and machine learning models for enterprise use cases across departments, including 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 performance monitoring.
Serve as a subject matter expert in responsible and explainable AI, promoting model fairness, transparency, and compliance with regulatory and ethical standards.
Partner with business and technology leaders to identify and prioritize AI use cases aligned with the organization’s transformation strategy.
Translate business challenges into analytical problems and lead cross-functional teams through data discovery, feature engineering, and algorithm development.
Use cloud-based data and AI platforms such as Snowflake, Azure ML, and Databricks to operationalize model delivery and integrate with enterprise data assets.
Mentor and coach staff through technical guidance, code reviews, and knowledge sharing.
Document model design assumptions, data sources, evaluation metrics, and deployment protocols.
Communicate technical results in accessible, actionable ways to executive and operational stakeholders.
Contribute reusable AI assets, libraries, and standardized templates to accelerate model development.
Stay current on emerging AI/ML technologies, frameworks, and healthcare analytics applications, and advise leadership on adoption opportunities.
Evaluate business operations and processes, identify opportunities for technical solutions, consult across business operations, and provide specialized knowledge and mentorship.
Ensure program deliverables meet departmental, organizational, and legislative policies, standards, and best practices.
Provide training, recommend process improvements, and mentor staff and interns as needed.
Perform other duties as assigned.