The opportunity
The Growth team drives user and revenue growth across ChatGPT’s consumer and business segments and other OpenAI products worldwide. We work across the full funnel—from awareness and acquisition through activation, retention, and expansion—using experimentation, data science,…
What you'll do
Build budget allocation algorithms. Decide how to distribute investment: across channels, campaigns, and markets using estimates of marginal returns, while accounting for diminishing returns, uncertainty, and business constraints.
Develop spend pacing and adaptive control systems. Adjust budgets and: spending rates as performance changes, balancing responsiveness with stability when feedback is delayed or noisy. Build in spend limits, monitoring, and safe fallback behavior.
Model marketing response and user value. Develop forecasts and models of: conversion, long-term value, and saturation that help determine where the next dollar can have the greatest impact.
Evaluate decisions rigorously. Use simulations, backtesting, and online: experiments to test allocation and control policies. Partner with Data Science to measure incrementality and validate whether offline improvements translate into better investment outcomes.
Own reliable production systems. Build data pipelines, training and inference: workflows, and integrations that turn model outputs into budget decisions. Establish data-quality checks, monitoring, and retraining with thoughtful treatment of privacy and consent.
Turn business goals into optimization problems. Partner with Growth: Marketing, Data Science, Engineering, and Finance to define objectives and constraints, communicate tradeoffs, and use evidence to improve investment decisions.
What they're looking for
- Help build a diverse and inclusive culture where people feel welcome,: challenge assumptions, and collaborate with candor and respect.
- + years of experience as a Machine Learning Engineer, Applied Scientist, Data: Scientist, or in a comparable quantitative engineering role, or an equivalent record of impact.
- Experience building and operating production ML or quantitative decision: systems, from problem formulation and data pipelines through deployment, experimentation, and monitoring.
- Strong software engineering skills, proficiency in Python and SQL, and: hands-on experience with ML frameworks and large-scale data systems.