Translates business needs into clear, testable requirements for AI and data products. Leads discovery workshops, process mapping, backlog refinement, business-rule definition, configuration sessions, and traceability from requirements through testing. Supports UAT, AI-output validation, defect triage, stakeholder sign-off, change control, and documentation. Requires strong requirements elicitation, process modeling, Agile delivery, SQL and data literacy, AI acceptance-criteria definition, communication, and workshop facilitation.
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.
- 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.
- 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.
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