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.