AI Solutions Program Manager, Finance

About Stripe Stripe builds programmable financial services. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Headquartered in San Francisco and Dublin, the company aims to increase the GDP of the internet.

Stripe

Help increase the GDP of the internet.

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S

stripe.com

5,001-10,000 employees

Technology, Information and Internet

South San Francisco, California

2010

Privately Held

1.8M LinkedIn followers

Raised $10.4B

• You'll work side-by-side with Finance subject matter experts to understand how
• work gets done, identify the highest impact opportunities, and partner with
• domain teams to chart a transformation plan which balances future ambitions with
• rapid impact.  You'll work independently to automate manual processes, design
• and build agents and required data solutions using internal platforms, and
• enable Finance teams to maintain these solutions long-term.
• You’ll build with your partner teams, not simply deliver solutions to them.
• You’ll help colleagues move from a manual workflow, to a first AI-enabled win,
• to a reliable solution they can confidently operate and improve themselves.
• Success means the workflow stays transformed after the initial build, the team
• trusts the output, and the people closest to the process can own what comes
• next.
• You’ll favor learning through building. When possible, you’ll put a useful
• prototype in front of a Finance user, test it against a real deliverable, and
• improve it quickly rather than spend weeks describing a theoretical future
• state. When you encounter platform limitations—which you will—you’ll find
• creative, supportable approaches, collaborate with Engineering to scope and test
• custom tools, and escalate strategically when needed.
• This role requires a blend of process automation, data operations, technical
• problem-solving, and stakeholder enablement. You'll spend your time building
• agents, optimizing data pipelines that feed them, creating knowledge layers that
• make them more accurate, and training Finance teams to use and maintain them.
• Success means Finance teams can independently run agents you've built while you
• provide light support and focus on the next automation opportunity.
• opportunities, and translate business, data, and control requirements into
• practical AI and automation solutions.
• Build and operationalize AI agents for Finance use cases, delivering
• end-to-end solutions from prototype through validation, monitoring,
• documentation, and handoff.
• Write and optimize SQL for data extraction, transformation, calculation, and
• validation, ensuring Finance-facing outputs are accurate, explainable, and
• reliable.
• Design reusable knowledge layers, evaluation methods, validation patterns,
• data-quality frameworks, and components that improve agent accuracy and
• accelerate future use cases.
• Identify agentic limitations and new possibilities; determine when to use
• existing capabilities, develop an alternative approach, or partner with
• Engineering to scope and test custom tools and integrations, including tools
• using Model Context Protocol where appropriate.
• Build alongside users, gather feedback through real deliverables, and iterate
• until the solution fits the workflow and earns user trust.
• Enable long-term ownership through clear SOPs, runbooks, training, and
• self-service tooling; coach Finance teams to operate, maintain, and evolve
• what has been built.
• Establish monitoring and observability for deployed agents, including
• metrics, alerting, and incident-response processes that support reliable
• operation.
• Diagnose technical and data issues, resolve problems independently where
• possible, and collaborate with Engineering when solutions require deeper
• platform changes.
• Measure adoption and impact, share lessons and wins, and turn successful
• implementations into standards, components, and playbooks for the broader
• Finance AI portfolio.

Required

• We're looking for someone who meets the minimum requirements to be considered
• for the role. If you meet these requirements, you are encouraged to apply. The
• preferred qualifications are a bonus, not a requirement.
• 5+ years of experience in data analytics, technical operations, business
• intelligence, automation, solutions delivery, or a related field.
• Of which, 2+ years of experience in program management, working directly
• alongside business leaders to develop and execute process transformation
• programs
• Hands-on experience building AI-enabled tools, agents, automations, or
• workflows that changed a real business process—not solely using AI as a
• conversational tool,
• Strong SQL proficiency, including experience writing complex queries using
• CTEs, window functions, and joins for data analysis, transformation, or
• pipeline logic.
• A track record of independently scoping and delivering technical solutions
• for process improvement, demonstrating ownership from problem definition
• through adoption.
• Strong analytical and investigative skills, including the ability to identify
• root causes, debug complex data problems, and resolve inconsistencies.
• Strong written and verbal communication skills, with the ability to explain
• technical concepts to non-technical audiences and work effectively with
• domain experts and Engineering partners.
• Experience teaching, coaching, enabling users, or transferring ownership of a
• solution through documentation, training, or self-service tools.
• Working knowledge of development practices such as version control, testing,
• code review, and iterative delivery.
• Experience using low-code tools, scripting, workflow automation, AI-assisted
• development, or custom integrations, paired with curiosity and the ability to
• learn evolving technologies.

Preferred

• (FP&A), Accounting, Treasury, Tax, Payments, or other areas.
• Familiarity with modern data tools and practices, such as Python, Databricks,
• ETL/ELT processes, data pipelines, and data-quality controls.
• Experience with Model Context Protocol or another extensibility or
• integration framework.
• Experience navigating financial systems and understanding how data flows
• through accounting, reporting, reconciliation, forecasting, or payments
• processes.
• Experience with change management, organizational transformation, or
• large-scale enablement programs.
• Experience building internal tools, templates, components, or playbooks that
• were adopted beyond the original team or use case.
• Experience working in a high-growth technology company with rapidly evolving
• processes and tools.
• Note on technical depth: This role involves working at the intersection of
• no-code AI tools and custom development. While software engineering expertise is
• not required, you should be comfortable applying technical concepts,
• collaborating closely with Engineering teams, and using AI-assisted development
• tools to extend platform capabilities when needed.