• Works with end-user requests to detail moderately to sophisticated business requirements, data definitions, business rules, and data quality requirements support of enterprise data intelligence projects. • Creates specifications for reports and analysis based on business/clinical needs and data elements required or available. • Produces data sets, reports, ad-hoc requests, dashboards, etc. • Conducts data analysis in support of a variety of analytic solutions. • Provide the specialized expertise needed to build, validate, and operationalize AI and analytics solutions in a secure healthcare environment. • Supports Azure Fabric migration by developing reusable data models, pipelines, semantic layers, AI-ready datasets, and governance-aligned reporting assets. • Accelerate delivery of predictive analytics, natural language processing, automation, anomaly detection, and self-service intelligence to reduce manual analysis, surface risks earlier, and improve decision speed across the organization.

• Perform enterprise-level statistical analysis to investigate trends, patterns, and relationships using statistical methods/tests to reach actionable conclusions. • Develop, train machine learning models to apply algorithms that support predictive analytics, classification tasks (e.g., land cover change), and operational forecasting • Develops enterprise-level information technology solutions by analyzing, coding, testing, and implementing new software and by modifying/enhancing existing software applications. • Coordinates with business analysts and/or users to determine requirements. Performs moderately complex analyses of system requirements and of existing business processes and information systems. • Collaborates with architects to determine the design of quality technical solutions. • Reviews systems under development and related documentation. • Resolves customers’ issues and responds to suggestions for improvements and enhancements. Participates in the development of software. • Performs code reviews and assists in more complex systems planning, scheduling and implementation.

• Develop and implement data processing workflows and pipelines to support solutions development and business intelligence needs. • Design and develop scalable and effective data solutions to support the flow and integration of data across systems. • Use big data technologies like Azure Data Lake, Synapse Analytics, and Data Factory to handle and analyze large volumes of data. • Implement data validation and cleansing procedures to ensure the highest level of data quality and integrity.

• Build robust automation processes for seamless data integration • Create and manage complex data workflows using Apache Airflow • Utilize MS Power Automate to streamline data processes • Develop customized SQL queries for efficient database solutions • Integrate multiple data sources into a customized data mart • Lead data science projects from problem definition to implementation • Meet with sales leaders to understand business challenges • Lead and manage complex projects ensuring timely delivery • Collaborate with cross-functional teams to achieve analytics goals