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OpenAIOpenAISan Francisco, CA

Machine Learning Data Scientist, Forecasting

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.

230k – 385k/yr
Hybrid7+ YOEData Science

About the role

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.

Skills

PythonSQLtime-series forecastingMachine Learningpredictive modelingscikit-learnPyTorchTensorFlowtimegptCausal Inferencebayesian forecastingmodel monitoringfeature engineeringmodel deploymentexplainability

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