Sr. AI Strategy and Operations Analyst

We are looking for someone to join our AI Strategy team to drive the adoption of AI and automation across business teams. This role will sit at the intersection of business operations, data, engineering, and AI, helping teams turn manual workflows into scalable, secure, and measurable AI-enabled processes.

What success looks like in this role :

Success means enabling teams across the organization to use AI safely and with increasing independence, replacing manual processes with repeatable workflows, scaling high-impact use cases across functions, and delivering measurable improvements in productivity, quality, reliability, and operational risk.

• Define and execute AI adoption roadmaps across business functions.
• Partner with Finance, Legal, HR, and other teams to identify high-value opportunities for AI, automation, and workflow improvement.
• Build monitoring and alerting for AI-powered data pipelines, integrations, and automations.
• Establish clear documentation for dashboards, metrics, data sources, ownership, and calculation logic.
• Operate an internal AI skills and automation program, including:
• User onboarding and enablement
• Contributor and skill-author approvals
• Security and governance reviews
• Adoption, usage, and quality tracking
• Build, launch and manage team-specific libraries of reusable AI skills, tools, and workflows.
• Develop and support internal AI champions and power users across the organization.
• Coordinate secure integrations between AI tools and enterprise systems, data platforms, APIs, spreadsheets, and third-party services.
• Partner with IT, Security, Engineering, Data, and business teams to establish the required cloud, source-control, identity, permissions, and service-account infrastructure.
• Develop reusable automation patterns for moving data between Python-based workflows, APIs, databases, spreadsheets, and internal systems.
• Translate operational business processes into reusable AI capabilities, including:
• Multi-source data reconciliation
• Query and scripting assistance
• Data-import troubleshooting
• Integration diagnostics
• Audit and evidence collection
• User-access and permissions reviews
• Reporting and operational workflow automation
• Interview business users to assess AI maturity, identify high-value use cases, surface capability gaps, and develop internal champions.
• Create practical data-classification and governance guidance for safe AI use.
• Define and track success metrics across adoption, quality, time savings, reliability, operational efficiency, and risk reduction.
• Build a repeatable onboarding framework and establish standards and reusable patterns that allow new teams to adopt AI safely and efficiently, and successful workflows to scale across teams rather than remaining isolated solutions.

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