Data Analytics & Management, AVP

State Street is one of the largest custodian banks and asset managers in the world, focusing on delivering trusted data, analytics, and AI solutions across Finance, Risk, and Treasury. The role of AVP in Data Analytics & Management involves building scalable AI solutions to tackle finance challenges, requiring strong analytical skills and the ability to connect technology choices to tangible finance outcomes.

Responsibilities:

• Build Agentic AI & Copilot Solutions for Finance
• Design and deliver agent‑based workflows that can plan, reason, and execute tasks across finance processes (with appropriate human oversight and controls)
• Implement solutions that use LLM copilots for finance narratives, variance explanations, exception triage, and root‑cause analysis
• Combine AI reasoning with deterministic logic (rules, thresholds, accounting constraints, materiality) to ensure reliability in controlled environments
• Create and refine prompts grounded in finance context (e.g., P&L, cost centers, accounting rules, materiality thresholds) and structure outputs for decision‑making
• Build reusable prompt patterns, evaluation approaches, and guardrails to reduce hallucinations and increase consistency
• Use analytics and data science methods (e.g., anomaly detection, classification, forecasting support, explainability) to strengthen finance insight and controls
• Analyze large, complex datasets to identify breaks, drivers, trends, and actionable signals relevant to Finance operations and reporting
• Use no‑code and low‑code tools (e.g., Alteryx, Power BI, Power Platform or similar) to: Operationalize AI outputs into finance workflows
• Orchestrate AI‑driven steps alongside rules‑based logic
• Surface AI‑generated insights, exceptions, and narratives to end users
• Know when low‑code is sufficient and when custom logic or data science is required
• Partner with Finance, Risk, and Treasury teams to understand processes end‑to‑end and design solutions that fit how work actually gets done
• Support rollout and adoption through documentation, training, and iteration based on user feedback
• Ensure solutions are explainable, auditable, and aligned with governance expectations
• Validate AI outputs against financial data and business logic; design monitoring to maintain quality over time