Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use.

Making data driven decisions is key to Plaid's culture. To support that, we need to scale our data systems while maintaining correct and complete data. We provide tooling and guidance to teams across engineering, product, and business and help them explore our data quickly and safely to get the data insights they need, which ultimately helps Plaid serve our customers more effectively.

Responsibilities

• Contribute towards the long-term technical roadmap for data-driven and machine learning iteration at Plaid
• Leading key data infrastructure projects such as improving ML development golden paths, implementing offline streaming solutions for data freshness, building net new ETL pipeline infrastructure, and evolving data warehouse or data lakehouse capabilities.
• Working with stakeholders in other teams and functions to define technical roadmaps for key backend systems and abstractions across Plaid.
• Debugging, troubleshooting, and reducing operational burden for our Data Platform.
• Growing the team via mentorship and leadership, reviewing technical documents and code changes.

Qualifications

• 5+ years of software engineering experience
• Extensive hands-on software engineering experience, with a strong track record of delivering successful projects within the Data Infrastructure or Platform domain at similar or larger companies.
• Deep understanding of one of: ML Infrastructure systems, including Feature Stores, Training Infrastructure, Serving Infrastructure, and Model Monitoring OR Data Infrastructure systems, including Data Warehouses, Data Lakehouses, Apache Spark, Streaming Infrastructure, Workflow Orchestration.
• Strong cross-functional collaboration, communication, and project management skills, with proven ability to coordinate effectively.
• Proficiency in coding, testing, and system design, ensuring reliable and scalable solutions.
• Demonstrated leadership abilities, including experience mentoring and guiding junior engineers.

Additional Information

Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable.

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Location

San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Strategic Finance

Compensation

• $230K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Strategic Finance team at OpenAI plays a critical role in shaping the company’s long-term trajectory. We partner closely with Product, Engineering, and Go-To-Market teams to inform high-stakes decisions through rigorous data science and economic modeling. As part of our expanding Data Science function, we’re building a best-in-class Forecasting capability to drive real-time, data-driven decision-making across user growth, revenue, compute infrastructure, and more.

We are developing scalable forecasting infrastructure to help us understand and anticipate business dynamics in an increasingly complex, usage-based world. Our models are foundational to planning, pricing, operational efficiency, and growth strategy - supporting key investment decisions and unlocking OpenAI’s full potential.

About the Role

We’re looking for a senior Machine Learning Data Scientist to lead our forecasting initiatives. You’ll be one of the founding members of the Forecasting pillar within Strategic Finance Data Science, responsible for building and scaling robust, interpretable, and production-ready forecasting systems. Your models will power critical business decisions by predicting core metrics such as DAU/WAU, revenue, LTV, compute consumption, and profitability.

This is a highly cross-functional role, requiring technical excellence, strong product intuition, and business acumen. You’ll collaborate with product managers, researchers, engineers, and finance leaders to operationalize forecasting insights, influence company-wide strategy, and build foundational forecasting capabilities at OpenAI.

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:

• Build statistical and machine learning models to solve forecasting needs across product, finance, infrastructure, and GTM domains.

• Own the end-to-end modeling lifecycle, including scoping, feature engineering, model development and prototyping, experimentation, deployment, monitoring, and explainability.

• Develop and productionize scalable, interpretable forecasts for user growth, monetization, compute load, customer lifetime value, and profitability.

• Contribute to self-service forecasting tools and internal platforms, enabling teams across OpenAI to access and act on real-time predictions.

• Research and evaluate emerging tools and techniques in the forecasting space, such as TimeGPT, large language model extensions, causal forecasting, and hybrid approaches.

• Drive strategic insight generation by translating technical outputs into business-aligned recommendations and decision frameworks.

• Collaborate closely with cross-functional teams to ensure forecasts are well-integrated into planning processes, experimentation workflows, and executive decision-making.

You might thrive in this role if you have:

• Advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Economics, Operations Research).

• 7+ years of experience in applied data science, with deep hands-on exposure to forecasting, predictive modeling, or marketplace systems.

• Expertise in time-series forecasting techniques and practical understanding of model trade-offs across performance, explainability, and scalability.

• Proficiency in Python, SQL, and tools such as scikit-learn, PyTorch/TensorFlow, and forecasting libraries.

• Demonstrated experience with model monitoring, debugging, and long-term maintenance in production environments.

• Strong communication and storytelling skills - able to simplify complexity and influence executive stakeholders.

• Self-directed, intellectually curious, and comfortable leading ambiguous projects from 0→1.

Bonus if

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