Machine Learning Data Scientist - Consumer Forecasting

OpenAI, Inc.; United States; Hybride; USD 190 000 - 230 000; Plein temps

Avantages offerts par ce poste

Relocation assistance

Hybrid work model

Résumé du poste

OpenAI, Inc. in San Francisco, CA is seeking a Senior Machine Learning Data Scientist to lead forecasting initiatives within Strategic Finance Data Science.

You will build and scale robust, interpretable, and production-ready forecasting systems powering decisions on DAU/WAU, revenue, LTV, and profitability. You’ll work cross-functionally with product, engineering, and finance, shaping forecasting strategy and operationalizing insights for real-time decision-making in a hybrid work environment

Qualifications

• Advanced degree (MS or PhD) in a quantitative field (Statistics, CS, Economics, OR)
• 7+ years of applied data science with forecasting focus
• Expertise in time-series forecasting and model trade-offs
• Proficiency in Python, SQL, scikit-learn, PyTorch/TensorFlow
• Experience with model monitoring and production maintenance
• Strong communication and storytelling for executive audiences
• Self-directed and comfortable leading ambiguous 0→1 projects

Responsabilités

• Build statistical and ML models for forecasting across product, finance, infra, and GTM
• Own end-to-end modeling lifecycle: scoping, feature engineering, development, prototyping, experiments, deployment, monitoring, explainability
• Develop scalable, interpretable forecasts for DAU/WAU, revenue, LTV, compute load, profitability
• Contribute to self-service forecasting tools and internal platforms for real-time predictions
• Research and evaluate emerging forecasting tools/methods like TimeGPT and causal forecasting
• Translate technical outputs into business-aligned recommendations for executives
• Collaborate with cross-functional teams to embed forecasts into planning and experimentation

Connaissances

Time-series forecasting

Python

SQL

scikit-learn

PyTorch

TensorFlow

Model monitoring

Communication

Leadership

Formation

MS or PhD in quantitative field

Outils

TimeGPT

Forecasting libraries

Description du poste

About the Team

The Strategic Finance team at the company 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 the company’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 the company.

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 the company 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 you have:

• Experience building or scaling forecasting platforms in a high-growth company.
• Familiarity with causal inference, Bayesian forecasting.
• Experience building agentic decision support systems.
• Passion for AI and a strong point of view on how machine learning should inform strategic decisions in fast-moving environments.

About the company

the company 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 the company’s Affi­

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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