We Are Looking For
Financial Services
DataAnalytics&Management,AVP
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Data Analytics & Management, AVP at We Are Looking For. Skills: Agentic AI, Copilot Solutions, Data Science, Finance. Design agent-based workflows. Reason across finance processes”
What You'll Achieve.
Improve insight quality; Improve controls; Improve efficiency; Improve decision-making
Industry & Context.
Analytical skills; Problem-solving skills; Root-cause analysis
What They're Looking For.
Must Have
Bachelor's degree in AI, Data Analytics, Computer Science, Engineering, or a related field, 5-7 years of experience in emerging AI technologies, data science, analytics, automation, Proficiency in SQL, Proficiency in Python
Nice to Have
Financial services experience preferred, PhD preferred, Microsoft Co-pilot Studio knowledge preferred, Microsoft 365 Co-pilot knowledge preferred, Databricks knowledge preferred, Anthropic/Claude knowledge preferred, Power BI/Tableau knowledge preferred, Alteryx knowledge preferred, Microsoft Fabric knowledge preferred, Microsoft Power App knowledge preferred, Microsoft Power Automate knowledge preferred
What You'll Do.
Design agent-based workflows
Reason across finance processes
Execute tasks across finance processes
Implement solutions using LLM copilots
Explain finance narratives
Triage finance exceptions
Analyze finance root-causes
Combine AI reasoning with deterministic logic
Create and refine prompts
Structure outputs for decision-making
Build reusable prompt patterns
Build evaluation approaches
Use analytics and data science methods
Strengthen finance insight
Strengthen finance controls
Identify breaks in data
Identify drivers in data
Identify trends in data
Identify actionable signals
Operationalize AI outputs
Orchestrate AI-driven steps
Surface AI-generated insights
Surface AI-generated exceptions
Surface AI-generated narratives
Understand finance processes end-to-end
Design solutions for finance workflows
Support rollout and adoption
Ensure solutions are explainable
Ensure solutions are auditable
Align solutions with governance expectations
Validate AI outputs against financial data
Monitor quality over time
How You'll Work.
Team & Collaboration
Partner with Finance teams; Partner with Risk teams; Partner with Treasury teams
Communication Scope
Written communication; Verbal communication
Full Job Description
**About the job** The Finance Data and AI Office (DART) delivers trusted data, analytics, and AI enabled solutions across Finance, Risk, and Treasury. This role sits within the Automation, Analytics & AI pillar and focuses on building practical, scalable AI solutions that address real finance challenges end to end—from problem framing and solution design through deployment and adoption. This position provides an excellent opportunity to interact with different business areas across State Street. **Who we are looking for** The ideal candidate combines strong analytical thinking with business curiosity and judgment and can thoughtfully apply the right mix of AI capabilities, agentic approaches, and low‑code/no‑code tools to design integrated, optimized solutions. Success in this role requires the ability to connect technology choices to tangible Finance outcomes—improving insight quality, controls, efficiency, and decision‑making. **What you will be responsible for:** 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 Prompt Engineering & Context Design (Finance‑Grade) * 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 Apply Data Science Where It Matters * Use analytics and data science methods (e.g., anomaly detection, classification, forecasting support, explainability) to strengthen finance insight
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