Business Analyst (with AI Experience)
Confirmed on the employer's own hiring board on Oct 20, 2026. First seen by Alion on Sep 17, 2026.
Responsibilities: Assess business processes to identify automation and AI opportunities, evaluating expected benefits, costs, feasibility, dependencies, and risks. Lead discovery sessions with business stakeholders to understand pain points, process variations, data availability, controls, and desired outcomes. Write clear functional requirements and user stories for AI-enabled features, including document intelligence and extraction, conversational assistants, classification and decisioning, and intelligent routing. Define what good looks like for AI features, including accuracy thresholds, confidence handling, fallback paths, human-in-the-loop processes, exception management, and audit requirements. Support business-side model evaluation by assessing AI outputs against real cases, helping structure representative evaluation datasets, documenting results, and identifying limitations. Translate AI findings into actionable recommendations, acceptance criteria, process changes, operating procedures, and implementation priorities. Clearly communicate AI capabilities, assumptions, risks, and limitations to non-technical stakeholders, and recognize when a rules-based or conventional solution is more appropriate. Collaborate with product, engineering, data, security, compliance, and operations teams throughout design, delivery, testing, rollout, and continuous improvement.
Requirements Hands-on experience working with AI-enabled products, prototypes, or solutions, with direct involvement in use-case discovery, requirements definition, testing, or evaluation. 5+ years of experience in business analysis, process improvement, product analysis, automation, digital transformation, or a related role. Strong ability to map business processes, identify inefficiencies, assess solution options, and build clear business cases covering value, cost, risk, and feasibility. Experience writing functional requirements, user stories, acceptance criteria, process flows, and exception scenarios for technology-enabled solutions. Practical understanding of AI concepts such as model outputs, confidence levels, evaluation datasets, accuracy, false positives and negatives, human oversight, and responsible fallback handling. Strong analytical and problem-solving skills, with the ability to assess AI outputs against business context and document limitations objectively. Excellent stakeholder management and communication skills, with the ability to explain complex AI concepts clearly to business and non-technical audiences. Ability to work collaboratively across business and technical teams; familiarity with data privacy, security, compliance, or audit considerations is an advantage