AI Business Analyst - QA, Officer

Overview

The AI Business Analyst will turn operational data into actionable intelligence for release management, end-to-end QA, operational deployments, and environment telemetry. The role bridges operational teams and AI-enabled delivery practices by translating raw signals into root-cause insights and automation backlogs.

What you'll do

• Analyze ServiceNow incident, request, change, and SLA/closure records to identify operational bottlenecks, delays, aging queues, rework loops, and ownership ambiguity.
• Mine Jira workflows to detect process misalignments, including issues that stay open after upstream/dependent tickets reach DONE, inconsistent transitions, missing closure evidence, and stale dependencies.
• Build repeatable query patterns, dashboards, exception reports, and data validation checks for tracking process friction and operational owners.
• Correlate telemetry data with ServiceNow and Jira records to link symptoms to operational actions, releases, configuration updates, deployment steps, or missing runbooks.
• Identify stability trends across environments, modules, clients, releases, patches, upgrades, and deployment windows.
• Document remediation steps, escalation paths, environmental checks, validation procedures, and closure criteria for recurring incidents and deployment failures.

What you'll need

• 10–12 years of experience in Business Analysis or an equivalent field.
• Hands-on experience analyzing workflows in ServiceNow, Jira, or similar enterprise ticketing/work-management platforms.
• Experience creating reusable AI prompts that interrogate large operational/functional datasets and produce clear executive metrics.
• Ability to translate technical findings into business-ready recommendations, runbooks, process maps, user stories, backlog items, acceptance criteria, or executive summaries.
• Comfort working in ambiguous operational environments where the analyst defines the problem and drives next actions.
• Understanding of responsible AI governance, usage standards, prompt design, retrieval-augmented generation concepts, data entitlement, source traceability, and audit-friendly AI documentation.

Details

• Location: Hyderabad, India.