About the job
About the Position
Join OpenAI as an Applied Data Scientist, focusing on the critical analysis of unit economics to drive sustainable growth and establish the company as a leader in impactful innovation.
In this role, you will spearhead the creation of advanced causal inference and data science models aimed at predicting and quantifying customer lifetime value (LTV). Your ability to translate complex data insights into strategic recommendations will be crucial for guiding our growth initiatives. This position demands a blend of technical expertise and executive communication skills.
This opportunity is based at our San Francisco headquarters, with a flexible hybrid work model (three days in the office per week). Relocation assistance is provided.
Your Vision
Develop robust causal inference and predictive analytics capabilities to assess and forecast LTV across various customer segments (B2C and B2B), and measure the incremental effects of diverse actions or product features on customer LTV.
Craft customer “happy paths” by identifying optimal adoption journeys that maximize lifetime value while ensuring customers derive the full benefits from our ecosystem.
Evaluate price elasticity to inform product packaging, monetization strategies, and pricing models.
Your Responsibilities
Collaborate with cross-functional teams (Finance, Product, Data Engineering, Go-to-Market, and other Data Science teams) to develop causal inference and predictive models that influence key business decisions.
Design and maintain LTV models across various product lines and customer cohorts.
Create scalable frameworks and models that make economic insights accessible to leadership and functional teams.
Support strategic pricing and investment decisions with thorough analytical and causal evidence.
Lead cross-functional data science initiatives, ensuring analytical precision, clarity, and timely execution.
Ideal Candidate Profile
Exceptional executive communication skills — ability to simplify complex analyses into clear, actionable insights for leadership.
Technical proficiency — expertise in ROI analysis, causal inference, statistical modeling, and ML predictive models; strong command of Python and SQL.
Strategic acumen — capacity to connect analytical insights to tangible business outcomes, providing the “so what” that drives leadership decisions.

