OpenAI is looking to build strategic unit economics understanding to guide sustainable growth and make it the most impactful company of its generation. This involves developing foundational causal inference and data science models to predict and quantify drivers of customer lifetime value (LTV) and translating these insights into strategic decisions and growth levers.
Requirements
- comfort spanning ROI analysis, causal inference, statistical modeling, and ML predictive models; strong experience with Python and SQL.
- Experience building scalable analytical frameworks and models that inform business decision-making.
Responsibilities
- Build causal inference and predictive analytics capabilities to measure and forecast LTV across customer segments (B2C and B2B), and quantify the incremental impact of different actions or product features on customer LTV.
- Design customer “happy paths” by identifying adoption journeys that maximize lifetime value while ensuring customers gain the most from our ecosystem.
- Analyze price elasticity to guide product packaging, monetization, and pricing strategies.
- Partner with cross-functional teams (Finance, Product, Data Engineering, GTM, and other DS teams) to build causal inference and predictive models that drive business decisions.
- Develop and maintain LTV models across product lines and customer cohorts.
- Architect scalable frameworks and models that democratize economic insights for leadership and functional teams.
- Support strategic pricing and investment decisions with robust analytical and causal evidence.
Other
- Executive communication — ability to distill complex analysis into clear, actionable recommendations for leadership.
- Strategic judgment — ability to connect analytical insights to business impact, delivering the “so what” that informs leadership decisions.
- Collaboration and ownership — thrive in a fast-paced, cross-functional environment and proactively take projects from concept to delivery.
- MS or PhD in a quantitative field (Statistics, Economics, Applied Math, Operations Research, Computer Science, etc.).
- 7+ years of experience in applied data science, causal inference, or quantitative strategy.
