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.

Requirements

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, passion for AI and a strong point of view on how machine learning should inform strategic decisions in fast-moving environments.

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.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

statistical modelingmachine learningforecastingfeature engineeringpredictive modelingtime-series forecastingmodel monitoringdebuggingscalabilitycausal inference

Soft Skills

communicationstorytellinginfluenceself-directedintellectually curiousleadershipcollaborationstrategic insight generationsimplifying complexitydecision-making

Certifications

MSPhD