Strategic Finance team plays a critical role in shaping the company's long-term trajectory
Partner closely with Product, Engineering, and Go-To-Market teams to inform high-stakes decisions through rigorous data science and economic modeling
Building a best-in-class Forecasting capability to drive real-time, data-driven decision-making across user growth, revenue, compute infrastructure, and more
Developing scalable forecasting infrastructure to understand and anticipate business dynamics in an increasingly complex, usage-based world
About the Role
Senior Machine Learning Data Scientist to lead forecasting initiatives
One of the founding members of the Forecasting pillar within Strategic Finance Data Science
Responsible for building and scaling robust, interpretable, and production-ready forecasting systems
Models will power critical business decisions by predicting core metrics such as DAU/WAU, revenue, LTV, compute consumption, and profitability
Highly cross-functional role, requiring technical excellence, strong product intuition, and business acumen
Based in San Francisco, CA. Hybrid work model of 3 days in the office per week, relocation assistance available
Responsibilities
Build statistical and machine learning models to solve forecasting needs across product, finance, infrastructure, and GTM domains
Own the end-to-end modeling lifecycle, including scoping, feature engineering, model development and prototyping, 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
Research and evaluate emerging tools and techniques in the forecasting space, such as TimeGPT, large language model extensions, causal forecasting, and hybrid approaches
Drive strategic insight generation by translating technical outputs into business-aligned recommendations and decision frameworks
Collaborate closely with cross-functional teams to ensure forecasts are well-integrated into planning processes, experimentation workflows, and executive decision-making
Requirements
Advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Economics, 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 model trade-offs across performance, explainability, and scalability
Proficiency in Python, SQL, and tools such as scikit-learn, PyTorch/TensorFlow, and forecasting libraries
Demonstrated experience with model monitoring, debugging, and long-term maintenance in production environments
Strong communication and storytelling skills
Self-directed, intellectually curious, and comfortable leading ambiguous projects from 0→1
Nice to Have
Experience building or scaling forecasting platforms in a high-growth company
Familiarity with causal inference, Bayesian forecasting
Passion for AI and a strong point of view on how machine learning should inform strategic decisions in fast-moving environments