AI Business Analyst - QA, Officer

Summary The AI Business Analyst will turn operational data into actionable intelligence for Release Management, Program Management Office, End-to-End QA, Operational Deployments, QA integrations, and environment telemetry.  This role is responsible for interrogating enterprise data sets across ServiceNow, Jira, telemetry platforms, logs, events, and databases to identify operational bottlenecks, workflow misalignments, stability patterns, and automation opportunities.  The AI Business Analyst will act as a bridge between operational teams and AI-enabled delivery practices: translating raw signals into root-cause insights, converting recurring manual activities into automation backlog items, documenting remediation runbooks, and ensuring that findings are mapped to the teams that own the relevant operational processes.

Analyze ServiceNow incident, request, change, catalog task, approval, SLA, and closure data to identify operational bottlenecks, recurring handoff delays, aging queues, rework loops, and ownership ambiguity across SaaS Operations teams. Mine Jira workflows to detect process misalignments such as linked Jira issues that remain open after upstream or dependent tickets reach defined DONE state, inconsistent transition usage, missing closure evidence, incomplete acceptance criteria, and stale dependencies. Develop repeatable query patterns, dashboards, exception reports, and data validation checks that help teams identify where process friction is occurring and which operational owners need to act. Translate findings into clear business narratives that explain impact, root cause, recommended actions, and expected operational benefit. Partner with telemetry analyst to mine logs, events, databases, system monitors, job schedules, and operational alerts for patterns that indicate application or platform instability, deployment risk, environment fragility, or recurring failure modes. Correlate telemetry findings with ServiceNow and Jira records to connect symptoms to operational actions, code changes, releases, configuration updates, deployment steps, or missing runbook guidance. Identify stability trends across environments, modules, clients, releases, patches, upgrades, and deployment windows; surface areas of instability before they become recurring production or client-impacting issues. Document operational remediation steps, escalation paths, environmental checks, validation procedures, and closure criteria for recurring incidents, deployment failures, batch issues, telemetry exceptions, and QA integration environment instability. Interface directly with end-to-end QA integration environment to document implementation plans, deployment steps, validation activities, environment dependencies, ownership boundaries, and readiness criteria. Produce environment workflow maps and implementation planning artifacts that support repeatability, onboarding, operational readiness, and future automation. Coordinate with other AI initiatives across the company to understand reusable patterns, governance expectations, prompt practices, data-source standards, entitlement models, responsible AI requirements, and operational support models. Help define practical AI use cases that improve SaaS Operations, deployment readiness, telemetry analysis, workflow compliance, and root-cause analysis without creating unmanaged operational risk. Technical Environment / Qualifications Minimum 10 - 12 years experience in Business Analysis / equivalent field. Strong business analysis experience in cloud based technologies (preferred), software delivery, release management, DevOps, QA, or operational process improvement. Hands on experience analyzing workflows in ServiceNow, Jira, or similar enterprise ticketing and work-management platforms. Experience creating AI prompts that are reusable, repeatable, interrogate large operational or functional data sets, and produce clear executive level metrics Experience translating