Applied AI Scientist IV - Healthcare, LLM/SLM, RAG experience

Job Summary:

The Applied AI Scientist IV is responsible for establishing technical direction and leading the design, development, and operationalization of advanced artificial intelligence solutions that improve healthcare operations, patient outcomes, and organizational performance. This role provides enterprise-wide technical leadership across machine learning, Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), and Natural Language Processing (NLP) initiatives.

The Applied AI Scientist IV establishes technical standards, mentors scientific teams, drives innovation, and serves as a strategic advisor for AI adoption and transformation efforts across the organization.

Essential Functions: Lead evaluation of emerging AI technologies and define adoption strategies that drive innovation and business value across the enterprise.

Utilize NLP algorithms and other deep learning techniques to process and analyze unstructured healthcare data, such as clinical notes, patient feedback, and medical literature, to extract meaningful insights.

Provide technical leadership for AI solutions from ideation through enterprise-scale production deployment in partnership with engineering and architecture teams.

Influence enterprise AI roadmaps through thought leadership, technical expertise, and strategic partnership with business and technology leaders.

Establish technical standards, reusable frameworks, and best practices for development, deployment, monitoring, and governance of AI solutions.

Define standards and best practices for operational readiness, evaluation, monitoring, governance, documentation, and post-deployment optimization of AI solutions.

Direct highly complex NLP, predictive analytics, and applied AI initiatives impacting multiple business domains and strategic priorities.

Work closely with interdisciplinary teams across IT, risk adjustment, program integrity, HEDIS, healthcare operations, finance, and clinical departments to identify and capitalize on opportunities for data-driven innovative solutions using cutting-edge emerging technologies.

Develop and implement predictive models, algorithms, and statistical techniques to extract insights from large and complex healthcare datasets.

Define and execute evaluation strategies for ML and LLM solutions, including quality metrics, bias and safety checks, and performance monitoring after deployment.

Utilize machine learning algorithms to identify patterns, trends, and opportunities for improving operational efficiency, cost containment, and patient care.

Conduct rigorous data analysis, including data cleansing, feature engineering, and exploratory data analysis, to derive meaningful insights and actionable recommendations.

Collaborate with stakeholders to define key performance indicators (KPIs), develop metrics, and create dashboards and reports that effectively communicate insights and support decision-making.

Provide strategic guidance and recommendations to senior leadership based on data analysis and predictive modeling results.

Ensure compliance with data privacy and security regulations, including HIPAA and PHI handling as applicable and maintain the highest standards of data integrity, model governance, and documentation.

Influence enterprise AI strategy through technical leadership across multiple programs, initiatives, and organizational priorities.

Serve as a recognized subject matter expert for AI, machine learning, NLP, Generative AI, and emerging technologies across the organization.

Mentor Applied AI Scientists, Data Scientists, and technical teams through code reviews, technical guidance, and architecture recommendations.

Evaluate Responsible AI practices, emerging healthcare AI regulations, and governance requirements to ensure compliant and trustworthy AI solutions.

Perform any other job related duties as requested.

• Education and Experience: Bachelor's degree in Data Science, Mathematics, Statistics, Engineering, Computer Science, or other related field required
• Master's degree preferred
• Equivalent years of relevant work experience may be accepted in lieu of required education
• Eight (8) years of experience in predictive analytics, data science, or a related field, preferably within the healthcare industry or managed care organizations, required
• Three (3) years of experience in the healthcare industry required
• Three (3) years of experience with cloud services (such as Azure, AWS or GCP) and modern data stack (such as Databricks or Snowflake) required
• Three (3) years of experience in development and deployment of an NLP and Generative AI solution in the healthcare industry required
• Experience providing technical leadership, architecture guidance, and mentoring scientific teams required

Competencies, Knowledge and Skills: Expert knowledge of Agentic AI, LLM architectures, prompt engineering, Retrieval-Augmented Generation (RAG), evaluation methodologies, and AI solution design

• Advanced expertise in MLOps and LLMOps practices including model deployment, monitoring, experiment tracking, reproducibility, CI/CD, and governance
• Advanced knowledge of model risk management, AI governance, Responsible AI, and healthcare AI regulatory considerations
• Expert knowledge of statistical learning, machine learning, predictive analytics, and scientific computing using Python and/or R
• Expert in data manipulation, data visualization, and SQL for data extraction and analysis
• Ability to perform advanced statistical analysis and modeling such as linear and non-linear regression, sampling, and Markov chains

Expertise designing document intelligence, OCR, and information extraction solutions utilizing modern AI technologies.

Expertise in AI solution architecture, API integration, and deployment of production-grade AI applications.

• Detailed knowledge of healthcare data, including medical and pharmacy claims, EMR data, HIE data, UM data, and demographic and population data
• Knowledge of healthcare operations, payer and provider models, and industry trends
• Proficient in feature engineering techniques and exploratory data analysis
• Excellent analytical, problem-solving, and critical-thinking skills
• Strong technical leadership, influence, and program coordination skills
• Excellent written and verbal communication and presentation skills
• Licensure and Certification: None
• Working Conditions: General office environment; may be required to sit or stand for extended periods of time
• Up to 15% (occasional) travel to attend meetings, trainings, and conferences may be required