Key skills
Data engineeringAI/ML workflowsHealthcare data expertiseSQLPythonETL/ELT toolsCloud data platformsNLP use casesBusiness acumenCollaborationProblem solvingAdaptabilityAIMLNLPLLMscikit-learnPandasMLOpsETLELTData EngineeringAnalyticsBIPower BISnowflakeDatabricksdbtAzureAgileScrumCI/CDCommunicationProblem Solving
About this role
SCAN is a nonprofit health organization dedicated to improving care for older adults. The role involves serving as a full-stack data and analytics engineer, designing and delivering end-to-end solutions to support AI/ML use cases and ensuring data readiness for analytics.
Responsibilities:
- Collaborate with business stakeholders across the health plan to translate business needs into actionable technical and AI/ML-enabled solutions
- Design, build, and maintain production-grade data pipelines using Azure Data Factory, Coalesce, Snowflake, and SQL to prepare data for analytics and ML workloads
- Engineer features and ML-ready datasets to support data scientists and AI engineers in model training, validation, and deployment for use cases like risk stratification, care management, and provider performance
- Train AI models and optimize their performance to ensure they meet business requirements
- Contribute to the design and implementation of AI/ML workflows, including Retrieval-Augmented Generation (RAG), NLP pipelines, Snowflake Cortex, and predictive models that improve plan operations and member experience
- Develop semantic data models and metric definitions to ensure consistent use of features, metrics, and business rules across AI/ML solutions and BI dashboards
- Create business-facing dashboards and applications (Power BI, Streamlit) that integrate predictive or AI-driven insights for decision-makers
- Support MLOps practices by helping implement automated testing, monitoring, and model versioning within CI/CD pipelines
- Optimize data and ML environments in Snowflake and Databricks for performance, cost efficiency, and scalability of AI/ML workloads
- Ensure governance and compliance by embedding lineage, PHI/PII protection, and Responsible AI considerations into data engineering workflows, consistent with CMS and HIPAA requirements
- We seek Rebels who are curious about AI and its power to transform how we operate and serve our members
- Actively support the achievement of SCAN’s Vision and Goals
- Other duties as assigned
Requirements:
- Required to work PST time zone
- Bachelor's degree in Computer Science, Data Analytics, or related field (or equivalent experience)
- 3–6 years of experience in data engineering, analytics engineering, or applied ML/AI
- Strong SQL skills and practical experience with modern cloud data platforms (Snowflake, Databricks, or similar)
- Hands-on experience with ETL/ELT tools (Azure Data Factory, Coalesce, dbt, or similar)
- Experience in healthcare data, particularly within Medicare Advantage health plans—claims, enrollment, provider, risk adjustment, and quality (HEDIS/Star ratings)
- Experience supporting AI/ML workflows—feature engineering, ML-ready datasets, and/or deploying models into production
- Strong familiarity with LLM or NLP use cases (RAG, embeddings, vector databases)
- Proficiency in Python for data and ML engineering (Pandas, PySpark, scikit-learn, or similar)
- Familiarity with agile delivery methodologies (Scrum/SAFe)
- Healthcare & Medicare Advantage expertise: Understands health plan data domains (claims, provider, member, risk adjustment, quality) and how analytics/AI/ML can improve outcomes
- AI/ML Enablement: Comfortable working side-by-side with data scientists and ML engineers to ensure models are production-ready, performant, and aligned to healthcare business needs
- Full-stack mindset: Ability to span the data lifecycle—back-end pipelines, semantic models, and front-end dashboards—integrating AI insights along the way
- Business acumen: Connects technical work to strategic health plan objectives, such as Stars improvement, member engagement, and cost management
- Problem solving: Resourceful in handling structured and unstructured healthcare data, able to design innovative solutions for AI/ML pipelines
- Collaboration & communication: Works seamlessly with business, analytics, and AI/ML colleagues; explains technical work to diverse stakeholders
- Adaptability: Learns emerging AI/ML tools and healthcare regulations quickly and applies them responsibly in production environments
- Experience with transformer models such as BERT