Business Analyst

Role summary: The Business Analyst turns business intent into clear, testable requirements for AI and data products, and keeps the backlog, stakeholders, and validation aligned from discovery through UAT. Experience: 4 to 6 years in business analysis, with at least 2 years on data, analytics, or AI/ML product delivery. Key responsibilities Lead discovery workshops to capture current-state processes, pain points, personas, and use case priorities. Write Business and Functional Requirement Documents (BRD/FRD), user stories, and acceptance criteria. Own backlog refinement with the Product Owner; prioritize work by business value and data readiness. Map end-to-end workflows, including approval, review, intake, and escalation flows across functions. Define business rules, thresholds, scoring logic, severity levels, and KPIs with SMEs. Facilitate configuration sessions and translate decisions into build specifications for engineers. Maintain traceability from requirements to build, test cases, and deliverables. Plan and support UAT: test scenarios, business validation of AI outputs, defect triage, and sign-off. Contribute to the RAID log, decision log, status reports, and change-control assessments. Produce user playbooks, process documentation, and handover materials. Required skills Strong requirements elicitation, process modeling (BPMN or similar), and gap analysis. Agile delivery with Jira, Azure DevOps, or similar tools; comfortable working in two-week sprints. Working SQL and data literacy: can read schemas, metadata, and data profiles to validate requirements. Ability to define acceptance criteria for AI features, such as accuracy, confidence, and evidence needs. Clear written and verbal communication with business, technical, and executive audiences. Skilled workshop facilitator who can align multiple stakeholders on one requirement set. Preferred skills Experience with GenAI, LLM, RAG, or agentic AI products, including human-in-the-loop design. Exposure to data governance concepts: metadata, data catalogs, taxonomies, data quality rules, lineage. Domain knowledge in pharma or life sciences (commercial, medical, regulatory, launch, or content review). Familiarity with BI tools (Power BI, Tableau) and dashboard requirement definition. Certification such as CBAP, CCBA, PMI-PBA, or CSPO.