div>The ideal candidate brings strong solution design capabilities, deep domain and technical expertise, and a proven ability to bring new products and features to market.
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li>Managing development teams in building healthcare AI and GenAI solutions, including analytical modeling, prompt engineering, Python-based development, testing, communication of results to clinical and operational stakeholders, front-end and back-end integration, and iterative use case development with health system clients;
Documenting and analyzing healthcare business processes - across clinical operations, and population health programs - to identify AI and GenAI opportunities, gather requirements, define initial hypotheses, and develop solution approaches tailored to health system workflows;
Collaborating with health system client teams - including clinical informatics, population health, and IT leaders - to understand their business and clinical problems and select the appropriate models, LLMs, and approaches for AI/GenAI use cases;
Designing and solutioning AI/GenAI architectures for health system clients, including RAG-based clinical knowledge retrieval systems, agentic AI workflows for care management and revenue cycle automation, and custom LLM application builds with appropriate PHI safeguards;
Managing teams to process healthcare unstructured and structured data - including clinical notes, discharge summaries, claims records, EHR data, and ADT feeds - for use as LLM context, including embedding of large clinical text corpora, generative SQL query development, and building connectors to EHR back-end databases;
Managing daily operations of a global healthcare data science team on client engagements, reviewing developed models, providing feedback, and assisting in analysis of clinical and operational outcomes;
Directing data engineers and other data scientists to deliver efficient, HIPAA-compliant solutions that meet health system client requirements for clinical, financial, and operational AI use cases;
Leading and contributing to development of proof of concepts, pilots, and production use cases for health system clients - spanning clinical decision support, prior authorization automation, patient risk scoring, workforce optimization, and throughput modeling - while working in cross-functional teams;
Facilitating and conducting executive-level presentations to health system leadership showcasing GenAI and ML solution capabilities, use case development progress, model performance, and recommended next steps;
Structuring, writing, communicating, and facilitating client presentations that translate complex AI and ML concepts into clear clinical and business value narratives for health system audiences; and,
Managing associates and senior associates through coaching, providing feedback, and guiding work performance, with an emphasis on developing healthcare domain knowledge alongside technical AI and ML capabilities. You will architect and build production-grade RAG pipelines, MCP connections, agentic AI workflows, and MLOps frameworks, managing daily operations across global delivery teams while engaging health system leaders at the executive level to ensure measurable clinical and operational impact.
Implementing data integration solutions using AWS Glue, AWS Lambda, Azure Data Factory, Azure Functions, GCP Functions, GCP Dataproc, Dataflow and other relevant services • Designing and managing data warehouses and data lakes, verifying data is organized and accessible • Monitoring and troubleshooting data pipelines, data warehouses and workflows to verify data quality, system reliability, performance and cost management • Implementing IAM roles and policies to manage access and permissions within AWS, Azure, GCP • Use AWS CloudFormation, Azure Resource Manager templates, Terraform for infrastructure as code (IaC) deployments • Use AWS, Azure and GCP DevOps services to build and deploy DevOps pipelines • Implementing data security practices using AWS, Azure, GCP, Snowflake or Databricks • Improving Cloud resources for cost, performance, and scalability • Proficiency in SQL and experience with relational databases • Proficient in programming languages such as Python, Java, or Scala • Familiarity with big data technologies like Hadoop, Spark, or Kafka is a plus • Experience with machine learning and data science workflows is a plus • Knowledge of data governance and data security practices • Demonstrating analytical, problem-solving, and communication skills • Having the ability to work independently and as part of a team in a fast-paced environment • Applying modern, cloud-based technology skills, ability to research emerging trends, analyst publications, and adoption of modern technologies in solution architectures • Collaborating and contributing as a team member: understanding personal and team roles, contributing to a positive working environment by building proven relationships with team members, proactively seeking guidance, clarification and feedback • Prioritizing and handling multiple tasks, researching and analyzing pertinent client, industry and technical matters, utilizing problem-solving skills, and communicating effectively in written and verbal formats to various audiences (including various levels of management and external clients) in a professional business environment • Coaching and collaborating with associates who assist with this work, including providing coaching, feedback and guidance on work performance. • Certification in Cloud Platforms [e.g., AWS Solutions Architect, AWS Data Engineer, Google Professional Cloud Architect, GCP Data Engineer Microsoft Azure Solutions Architect, Azure Data Engineer Associate, or Snowflake Core, Snowflake Databricks Data Engineer Associate] is a plus • Designing and implementing thorough data architecture strategies that meet the current and future business needs • Developing and documenting data models, data flow diagrams, and data architecture guidelines • Verifying data architecture is compliant with data governance and data security policies • Collaborating with business stakeholders to understand their data requirements and translate them into technical solutions • Evaluating and recommending new data technologies and tools to enhance data architecture • Building, maintaining, and improving ETL/ELT pipelines for data ingestion, processing, and storage across batch and real-time data processing • Building, maintaining, and improving Data Quality rules leveraging DQ tools and/or other ETL/ELT tools • Developing and deploying scalable data storage solutions using AWS, Azure and GCP services such as S3, Amazon RDS, DynamoDB, Azure Data Lake Storage, Azure Cosmos DB, Azure SQL DB, GCP Cloud Storage etc.