AI & Process Automation Analyst (Must reside/work within firm footprint)

Position summary

Core responsibilities

• Build, test, and implement automation and AI-enabled solutions using Python, Power Automate, Power Platform, Copilot, and other approved tools.
• Automate recurring reporting, data collection, notifications, and administrative processes.
• Develop self-contained AI use cases, including Copilot workflows, prompt assets, and document or knowledge tools.
• Create business requirements and solution-design documentation covering business purpose, data sources, logic, controls, and support needs.
• Support solutions after go-live and identify when enterprise teams should scale them.
• Use approved agentic coding tools, including CoPilot, Cowork, or Claude Code, for development, debugging, and testing under firm policy.
• Analyze data sources and flows; use Python and SQL to extract, clean, validate, and connect data.
• Document data flows and dependencies for maintenance and future development.
• Partner with process owners to identify pain points and simplify workflows before automating them.
• Develop test cases, perform initial testing, support user acceptance testing, and assist business partners with validation.
• Support Model Risk Management review, validation, documentation, and governance when needed.
• Support intake and prioritization of automation and AI ideas; track hours saved, errors reduced, and cycle-time improvements.

Minimum qualifications

• Bachelor’s degree or equivalent combination of education and practical experience.
• At least five years of experience in automation, data analysis, business intelligence, or software/scripting development.
• Working proficiency in coding, scripting, or query languages such as Python and SQL.
• Experience with at least one low-code or automation platform, such as Power Automate or Power Apps.

Tools and work practices

• Maintain current knowledge of approved automation, AI, and low-code tools, platforms, and firm policies.
• Build effective working relationships across Operations, Governance, Analytics, Technology, QA, and Managed Services.

Risk and service expectations