Quality Assurance & Testing: Execute user acceptance testing (UAT) for new automations and integrations, document test cases, verify edge-case handling, and monitor post-deployment error rates to ensure production stability.
System Operations & Backlog Management: Triage, investigate, and prioritize internal support tickets, bugs, and feature requests while ensuring projects meet delivery timelines and SLAs.
Impact Tracking & Analytics: Analyze and monitor operational dashboards tracking system adoption, process efficiency (hours saved, error reduction, SLA compliance), and automation health. Produce weekly reports, proactively flag regressions, and present data-driven recommendations in team huddles.
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Coordinate cross-functional delivery — align business analysts, developers, QA, vendors, and operations teams around a shared plan and shared milestones.
Apply testing-phase knowledge — understand unit, integration, system, regression, and UAT testing phases well enough to plan realistic test cycles and set clear exit criteria for each stage.
Partner with vendor and QA teams — work closely with vendor delivery teams and internal QA to make sure test coverage spans all angles — functional, integration, performance, security, and regression — not just the happy path.
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Support the data science/analytics project lifecycle, including problem definition, data sourcing and cleaning, exploratory analysis, basic model prototyping (where relevant), and documentation of results.
Assist in building or improving dashboards and reporting (e.g., in Power BI) to help stakeholders monitor performance, service metrics, and operational outcomes.
Prepare and maintain clear documentation describing the steps, logic, controls, and maintenance approach for automated solutions (including process notes, runbooks, and user guides where applicable).
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