Why This Job is Featured on The SaaS Jobs
Financial Engineering sits at the heart of any subscription SaaS business, where pricing, checkout, payments, and renewals determine whether product demand converts into durable revenue. This role is notable because it focuses on the “revenue plumbing” that underpins global self-serve and enterprise motions, spanning subscriptions, payment methods, and the operational data needed to keep monetization systems reliable at scale.
For a SaaS data scientist, the career upside is the chance to build judgment around metrics that actually govern recurring revenue businesses: conversion, churn, payment failure rates, and the tradeoffs between growth, risk, cost, and user experience. Owning experimentation and source-of-truth datasets in this domain develops transferable skills across SaaS companies, including causal measurement, launch readiness analytics, and partnering patterns with product, engineering, finance, and risk stakeholders.
This position tends to suit professionals who like applied analytics close to production systems, not just reporting. It will fit someone comfortable translating ambiguous business questions into measurable outcomes, and who enjoys working cross-functionally to influence product decisions through experimentation design, operational visibility, and clear communication of tradeoffs.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the Team
OpenAI’s Financial Engineering (FinEng) team powers how revenue flows through our products—pricing & packaging, checkout, payments, subscriptions, and the financial infrastructure behind them. We partner with Product, Engineering, Risk, Finance, and Go-to-Market to make paying for OpenAI products seamless, reliable, and efficient worldwide.
About the Role
As a Data Scientist on FinEng, you’ll own the analytics and experimentation that improve our checkout and payments, subscriptions, and pricing & monetization systems. You’ll define the metrics that matter, build the source-of-truth data assets, and design experiments that increase conversion, reduce churn and payment failures, and expand global payment method coverage. Your work will directly influence revenue, customer experience, and how we scale internationally.
This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
In this role, you will
Own checkout & payments analytics and experimentation across methods and locales (e.g., bank transfers, emerging rails), improving conversion while monitoring risk and latency.
Build and run the experimentation program for in-house checkout—define success metrics and guardrails, execute staged rollouts, and use offline incrementality when online tests aren’t feasible.
Create operational visibility and source-of-truth data with FinEng Data Engineering—land team-level metrics, SLAs, and self-serve dashboards that drive proactive action.
Lead subscription, retention, and monetization analytics—ship launch-readiness for new subscription features, reduce involuntary churn (e.g., targeted retrials/nudges), and develop elasticity/FX frameworks toward pricing optimality.
You might thrive in this role if you have
5+ years in a quantitative role (data science, product analytics, or experimentation) in high-growth or fintech environments
Fluency in SQL and Python, with a track record designing and interpreting A/B tests and quasi-experiments
Experience building product metrics from scratch and operationalizing them for decision-making
Excellent communication skills with PMs, engineers, risk/finance partners, and executives
Strategic instincts beyond significance tests—clear thinking about tradeoffs (conversion vs. risk vs. cost vs. user experience)
You could be an especially great fit if you have
Payments, checkout, or subscription analytics experience (PSPs, bank rails, disputes/refunds, risk, e-commerce)
Background in offline incrementality methods, uplift modeling, CUPED/causal inference, or counterfactual evaluation
Experience with internationalization/local payments, FX, and pricing & packaging strategy
Comfort building operational analytics (alerting, SLIs/SLOs) and partnering closely with data engineering
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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