AI Solutions Program Manager, Finance
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). However we should only include sections that have real specific content. We must not repeat job title as heading. We must not put Job ID etc. We must remove boilerplate like EEO etc. We must preserve all substantive info. Use - for any list of duties/requirements/benefits, and
for prose.
We need to decide which sections to include. The headings we can use: About the role (maybe combine Who we are, About The Team, What you’ll do). Responsibilities (list). Requirements (minimum requirements). Qualifications (preferred qualifications). Skills? Not explicit separate. Benefits (Pay and benefits). Pay (maybe separate but we can combine). Schedule (In-office expectations). We should not create sections with filler like "commensurate with experience". We have actual content for Pay and benefits: salary range and benefits list. In-office expectations: schedule info.
We need to ensure we don't create empty sections. Use
for each section heading.
We must output only HTML fragment, no /, no markdown fences.
Let's structure:
About the role
... combine Who we are About Stripe, About The Team, What you’ll do.
Then Responsibilities: Responsibilities
- ... each bullet from Responsibilities list.
Then Requirements: Requirements
- ... each bullet from Minimum Requirements.
Then Qualifications: Qualifications
- ... each bullet from You Will Also Likely Have (not Required).
Then Benefits: Benefits
- ... list benefits: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; wellness stipends.
Then Pay: Pay
The annual US base salary range for this role is $147,200 - $220,800. ... include note about OTE etc.
Then Schedule: Schedule
In-office expectations: Office-assigned Stripes in most locations expected to spend at least 50% of time in office; some roles 100% office; etc.
We need to preserve all substantive info. Let's extract details.
First, About the role content:
From "Who we are About Stripe": Stripe is a financial infrastructure platform for businesses. 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. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.
"About The Team": You'll be joining the Finance Operational Excellence team, reporting to the Head of Finance Operational Excellence. We are finance transformation and AI experts working side-by-side with Finance, Product, and Engineering teams to redefine processes and build the finance organization of the future. This is an opportunity for someone who enjoys working across disciplines: part solution developer, part data architect, part workflow designer, and part coach. You’ll have room to shape how applied AI is built and scaled both from a technical and end-user perspective.
"What you’ll do": 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.
We can combine these into a few paragraphs.
Now Responsibilities list: Let's extract each bullet from the Responsibilities section (starting after "Responsibilities").
The text:
"Embed with Finance teams to diagnose workflows, identify high-leverage 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."
We'll make each a - .
Now Minimum Requirements list:
"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."
We'll make each a
- .
Now Qualifications (You Will Also Likely Have (not Required)) list:
"Domain experience in Finance Operations, Financial Planning & Analysis (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."
Make each
- .
Now Benefits list: "Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends."
We can split into items: equity; company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; wellness stipends.
Now Pay: The paragraph about salary range and note.
We need to include the note: "The annual US base salary range for this role is $147,200 - $220,800. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location. Applicants interested in this role and who are not located in the US may request the annual salary range for their location during the interview process."
We'll put that in
.
Now Schedule (In-office expectations): The paragraph:
"In-office expectations Office-assigned Stripes in most of our locations are currently expected to spend at least 50% of the time in a given month in their local office or with users. This expectation may vary depending on role, team and location. For example, Stripes in Stripe Delivery Center roles in Mexico City, Mexico, Bengaluru, India, and Dublin, Ireland work 100% from the office. Also, some teams have greater in-office attendance requirements, to appropriately support our users and workflows, which the hiring manager will discuss. This approach helps strike a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility when possible."
We'll put that in
.
Now we must ensure we don't include boilerplate like "Note on technical depth". That section is substantive? It says: "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." This is substantive; we could include it maybe under a section like "Technical depth". But the instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. It didn't list "Technical depth". However we could incorporate that note into About the role or Responsibilities? It's about technical depth; maybe we can add a section "Technical depth" but not in allowed list? The instruction: "Use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." The e.g. suggests those are examples, not exclusive. But we should
for prose. We need to decide which sections to include. The headings we can use: About the role (maybe combine Who we are, About The Team, What you’ll do). Responsibilities (list). Requirements (minimum requirements). Qualifications (preferred qualifications). Skills? Not explicit separate. Benefits (Pay and benefits). Pay (maybe separate but we can combine). Schedule (In-office expectations). We should not create sections with filler like "commensurate with experience". We have actual content for Pay and benefits: salary range and benefits list. In-office expectations: schedule info. We need to ensure we don't create empty sections. Use
for each section heading.
We must output only HTML fragment, no /, no markdown fences.
Let's structure:
About the role
... combine Who we are About Stripe, About The Team, What you’ll do.
Then Responsibilities:Responsibilities
- ... each bullet from Responsibilities list.
Requirements
- ... each bullet from Minimum Requirements.
Qualifications
- ... each bullet from You Will Also Likely Have (not Required).
Benefits
- ... list benefits: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; wellness stipends.
Pay
The annual US base salary range for this role is $147,200 - $220,800. ... include note about OTE etc.
Then Schedule:Schedule
In-office expectations: Office-assigned Stripes in most locations expected to spend at least 50% of time in office; some roles 100% office; etc.
We need to preserve all substantive info. Let's extract details. First, About the role content: From "Who we are About Stripe": Stripe is a financial infrastructure platform for businesses. 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. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. "About The Team": You'll be joining the Finance Operational Excellence team, reporting to the Head of Finance Operational Excellence. We are finance transformation and AI experts working side-by-side with Finance, Product, and Engineering teams to redefine processes and build the finance organization of the future. This is an opportunity for someone who enjoys working across disciplines: part solution developer, part data architect, part workflow designer, and part coach. You’ll have room to shape how applied AI is built and scaled both from a technical and end-user perspective. "What you’ll do": 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. We can combine these into a few paragraphs. Now Responsibilities list: Let's extract each bullet from the Responsibilities section (starting after "Responsibilities"). The text: "Embed with Finance teams to diagnose workflows, identify high-leverage 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." We'll make each a. Now Schedule (In-office expectations): The paragraph: "In-office expectations Office-assigned Stripes in most of our locations are currently expected to spend at least 50% of the time in a given month in their local office or with users. This expectation may vary depending on role, team and location. For example, Stripes in Stripe Delivery Center roles in Mexico City, Mexico, Bengaluru, India, and Dublin, Ireland work 100% from the office. Also, some teams have greater in-office attendance requirements, to appropriately support our users and workflows, which the hiring manager will discuss. This approach helps strike a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility when possible." We'll put that in
. Now we must ensure we don't include boilerplate like "Note on technical depth". That section is substantive? It says: "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." This is substantive; we could include it maybe under a section like "Technical depth". But the instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. It didn't list "Technical depth". However we could incorporate that note into About the role or Responsibilities? It's about technical depth; maybe we can add a section "Technical depth" but not in allowed list? The instruction: "Use