Sr. Data Analyst (AI & Business Intelligence)

Requirements and Testing

• Work with program areas and project sponsor to gather business requirements and translate into technical specifications.
• Lead as Test Manager creating test plans and test cases that integrate from Agile sprint cycles, epics and stories, as well as Waterfall project plans and all project artifacts
• Translate complex data requests and operational requirements into clear, actionable queries for AI against complex analytics data sources.
• Explain technical findings and data limitations in simple, non-technical language to end-users.

AI Prompting and Data Synthesis

• Develop and refine effective AI prompts and query strategies to retrieve and synthesize data accurately from complex datasets.
• Guide non-technical users in crafting precise prompts to get the data they need, ensuring fidelity and accuracy.
• Develop a library of standardized prompts and query templates for common reporting needs.

Data Reporting and Visualization

• Extract, integrate, and analyze data from multiple complex internal and external sources to support HHS program needs.
• Collaborate with end-users and performance analysts or IT internal leaders to create and validate reports, dashboards, and data visualizations for program monitoring and official reporting.
• Provide subject matter expertise on validating output from AI, particularly with respect to identifying and mitigating hallucinations.
• Ensure all data outputs adhere to agency reporting standards, data governance, and compliance regulations.

Data Literacy and Training

• Champion data literacy across the organization by developing and conducting AI training sessions for non-technical staff.
• Create clear, comprehensive documentation and tutorials on using AI tools for data synthesis.
• Promote a data-driven culture by enabling and empowering all employees to effectively utilize data and AI.

Collaboration and Problem-Solving

• Work closely with data engineering, IT, and Program teams to troubleshoot data-related issues and address inconsistencies and mitigation strategies.
• Provide expert guidance to program staff on interpreting data trends and answering complex data questions.
• Stay up to date on new AI and data analytics tools and techniques to continuously improve data access, data quality and reporting.