Level

State Street

Data Analytics & Management, Officer

AI in this role

claudeanthropiccopilotdatabricks
prompt-engineeringai-safety

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 solutions that address real finance challenges end to end—from problem framing and solution design through deployment and adoption—including data governance and internal control capabilities that align to regulatory expectations and standards.

Who we are looking for

The ideal candidate combines strong analytical thinking with business curiosity and judgment and can thoughtfully apply emerging AI capabilities and low‑code/no‑code tools to build data solutions with 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 and controls.
  • Analyze large, complex datasets to identify breaks, drivers, trends, and actionable signals relevant to Finance operations and reporting.

Enable AI Using No‑Code / Low‑Code Platforms

  • 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


Responsible AI & Controls‑Aware Delivery

  • 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.

Regulatory Alignment, Data Governance & Controls

  • Contribute to BCBS 239 and broader regulatory aligned outcomes by improving traceability, accuracy, completeness, timeliness, and evidencing for critical finance/risk data used in aggregation and reporting
  • Ensure solutions delivered are consistent with broader regulatory expectations and embed appropriate data governance and controls from design through production

Skills and experience needed:

  • Bachelor’s degree in AI, Data Analytics, Computer Science, Engineering, or a related field.
  • 3-5 years of experience in emerging AI technologies, data science, analytics, automation (financial services preferred)
  • Strong analytical and problem‑solving skills
  • Proficiency in SQL and Python
  • Familiar with data warehousing, data modelling, ETL concepts
  • Exposure to automation tools (e.g., low‑code platforms, RPA, workflow tools).
  • Understanding of Finance, Risk and/or Treasury business processes
  • Knowledge of data governance, data quality, and regulatory compliance concepts (e.g., BCBS 239 principles)
  • Strong written and verbal communication skills
  • Knowledge of the below tools is preferrable
  • Microsoft Co-pilot Studio
  • Microsoft 365 Co-pilot
  • Databricks
  • Anthropic/Claude
  • Alteryx
  • Microsoft Fabric

About State Street

Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.

We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.

As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.

Discover more information on jobs at StateStreet.com/careers

Read our CEO Statement

How we rate this

Data Analytics & Management, Officer at State Street rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringAI SafetyClaudeAnthropicCopilotDatabricks

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How do you think about the risk of an AI system in this kind of role failing silently?
  3. What are the limits of Claude that you've run into, and how did you work around them?
  4. What's a project where you used Anthropic hands-on?
  5. Walk me through how you've used Copilot in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Prompt Engineering, AI Safety, Claude, Anthropic, and Copilot. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.

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