Senior AI Scientists

Job Duties

• AI Strategy, Portfolio Leadership, and Enterprise Direction
• Architecture and Delivery of Enterprise AI, GenAI, and ML Solutions
• AI Platform Engineering Using Snowflake Cortex and Cloud Technologies
• Clinical AI Deployment and Operationalization in Epic Ecosystems
• MLOps, LLMOps, and Production Governance
• Responsible AI, Compliance, and Data Governance
• Technical Leadership, Mentorship, and AI Capability Building
• Cross Functional Collaboration and Executive Communication
• Innovation, Continuous Improvement, and Industry Leadership

Requirements

• Bachelor’s degree in Data Analytics, Computer Science, Mathematics, Statistics, Economics, Physics, or biomedical informatics
• 5 years as Healthcare analytics, data analyst, data scientist, or graduate assistant
• Epic certifications, including: Clarity Data Model, Clinical Data Model, Cogito, Cosmos for Data Scientist, and Cosmos Data Model for Data Architect
• Advanced ability to design, architect, and explain artificial intelligence (AI) solutions, including machine learning and generative AI systems, to both technical team members and nontechnical stakeholders such as clinicians, operational leaders, and executives
• Experience using enterprise analytics and AI platforms, specifically Snowflake and Snowflake Cortex to perform largescale data processing, feature engineering, vector search, and model or AI application deployment
• Experience deploying and supporting AI or machine learning models within Epic Nebula ensuring integration with clinical workflows and operational systems
• Experience establishing or following advanced AI development and operations practices, including model and prompt versioning, monitoring of performance and drift, documentation (e.g., model cards), and controlled deployment into production systems
• Strong foundation in applied statistics and advanced analytics, including regression analysis, hypothesis testing, experimental or observational study design, and interpretation of results for decision making
• Experience working with largescale enterprise datasets, such as electronic health records (EHRs), clinical systems, or operational databases, and converting raw data into structured analytics datasets suitable for AI modeling
• Knowledge of data governance, security, and privacy standards, particularly in regulated environments such as healthcare, including compliance with HIPAA and protection of sensitive patient data
• Experience providing technical leadership in AI initiatives, including advising on AI strategy, evaluating emerging AI technologies, and guiding adoption of advanced AI capabilities across business or clinical domains