AI Business Analyst - AI Center of Excellence

Position Overview The AI Business Analyst serves as the bridge between business stakeholders and technical delivery teams. You will translate business outcomes into well‑defined AI/ML use cases, shape product backlogs, and ensure initiatives are feasible, ethical, and positioned for measurable ROI. This role requires comfort context‑switching across multiple projects in a fast‑moving AI COE. Team / Department You will join the AI Center of Excellence (COE), partnering closely with product managers, data scientists, ML engineers, platform engineers, enterprise architects, and business leaders across functions. Project Landscape • Work concurrently on multiple AI initiatives (discovery, prototyping, and productionization) • Mix of GenAI (LLM‑powered applications, copilots, agents) and traditional ML (forecasting, classification, recommendations) • Focus on high‑value, pragmatic use cases with clear business outcomes and adoption plans Key Responsibilities • Discovery & Framing: Lead workshops and stakeholder interviews to elicit goals, success metrics, constraints, and change‑management needs. Convert ideas into testable hypotheses. • Requirements & Artifacts: Document current/future state process maps, user journeys, use‑case charters, data and model requirements, and non‑functional needs (security, privacy, performance). • Backlog Ownership Support: Create and refine epics, user stories, and acceptance criteria; partner with product/delivery leads on prioritization using value/effort, risk, and ROI lenses. • Feasibility & Risk: Collaborate with data science/engineering to assess data availability/quality, model feasibility, technical spikes, and dependencies; surface ethical, regulatory, and IP risks. • Experimentation: Define evaluation approaches (A/B tests, offline/online metrics), prompt‑evaluation frameworks for GenAI, and success criteria for pilots and MVPs. • Value Realization: Establish baseline metrics and target outcomes; track benefits (e.g., productivity, revenue lift, risk reduction) from pilot through scale‑up. • Delivery Support: Facilitate sprint ceremonies, unblock decisions, harmonize stakeholder feedback, and ensure traceability from requirements to working features. • Adoption & Change: Partner with change‑management and enablement to design rollout, training, and feedback loops; gather qualitative/quantitative signals post‑launch. • Governance & Compliance: Align with data governance, model risk management, security, and responsible‑AI standards; ensure documentation is audit‑ready. Required Qualifications • 4–8 years in business analysis, product, or consulting roles with at least 2+ years supporting AI/ML or data‑product initiatives. • Strong process mapping (BPMN/Lean/Six Sigma concepts) and stakeholder facilitation skills across business and technical audiences. • Hands‑on experience translating business problems into AI/ML use cases, user stories, and acceptance criteria. • Familiarity with GenAI tools (e.g., LLM application patterns, prompt design, retrieval‑augmented generation) and experimentation frameworks. • Solid understanding of data fundamentals (sources, pipelines, quality, privacy) and model evaluation basics (precision/recall, business KPIs). • Proficiency with Agile delivery tools (e.g., Jira/Azure DevOps) and documentation platforms (e.g., Confluence). Preferred Qualifications • Exposure to MLOps/LLMOps concepts (feature stores, CI/CD for models, monitoring, guardrails, prompt/version management). • Experience shaping ROI models, business cases, and value tracking for AI initiatives. • Background in one or more domains (e.g., operations, finance, marketing, customer service, supply chain). • Knowledge of responsible‑AI practices (bias, explainability, safety) and relevant regulations. • Comfort with light technical artifacts (e.g., drafting data dictionaries, mapping APIs, defining evaluation datasets).