# Machine Learning Data Scientist, Forecasting

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** Data Science
**Salary:** $230k – $385k/yr
**Experience:** 7+ years
**Skills:** Python, SQL, time-series forecasting, Machine Learning, predictive modeling, scikit-learn, PyTorch, TensorFlow, timegpt, Causal Inference, bayesian forecasting, model monitoring, feature engineering, model deployment, explainability
**Posted:** 2026-08-12

> Leads forecasting initiatives by building interpretable, production-ready statistical and machine learning models for growth, revenue, compute, and profitability. Requires an advanced quantitative degree, 7+ years of applied data science experience, and strong expertise in time-series forecasting.

## Job Description

## Responsibilities
- Build statistical and machine learning models for forecasting needs across product, finance, infrastructure, and go-to-market domains.
- Own the end-to-end modeling lifecycle, including scoping, feature engineering, model development, 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 that enable teams to access and act on real-time predictions.
- Research and evaluate forecasting techniques and tools, including TimeGPT, large language model extensions, causal forecasting, and hybrid approaches.
- Translate technical outputs into business-aligned recommendations and decision frameworks.
- Collaborate with cross-functional teams to integrate forecasts into planning processes, experimentation workflows, and executive decision-making.

## Requirements
- Advanced degree (MS or PhD) in a quantitative field such as Statistics, Computer Science, Economics, or 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 trade-offs among performance, explainability, and scalability.
- Proficiency in Python, SQL, scikit-learn, PyTorch or TensorFlow, and forecasting libraries.
- Experience with model monitoring, debugging, and long-term maintenance in production environments.
- Strong communication and storytelling skills, with the ability to simplify complexity and influence executive stakeholders.
- Ability to lead ambiguous projects from 0 to 1.

## Nice-to-haves
- Experience building or scaling forecasting platforms in a high-growth company.
- Familiarity with causal inference and Bayesian forecasting.
- Passion for AI and a strong perspective on how machine learning should inform strategic decisions.

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