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Vercel is seeking a Staff Applied Scientist to lead consumption forecasting and build the ML systems that power financial planning, infrastructure investment, and executive decision-making across the platform. This is a staff-level technical leadership role where you will architect Vercel's end-to-end consumption forecasting systems across compute, bandwidth, edge functions, storage, and emerging products.
You will design and productionize advanced ML approaches for time-series forecasting, including deep learning-based methods, probabilistic/Bayesian approaches, and hierarchical statistical-ML architectures that go beyond off-the-shelf solutions. You'll develop multi-horizon forecasting systems spanning operational to quarterly to long-range planning, with hierarchical architectures that reconcile predictions across account, cohort, segment, and global levels.
Key responsibilities include building ML infrastructure for backtesting, monitoring, drift detection, and forecast explainability; developing scenario simulation and causal inference frameworks to evaluate pricing changes and product launches; and partnering directly with Finance leadership on board-level reporting and revenue planning. You'll also work with Infrastructure Engineering on capacity planning and cost optimization, and collaborate with Product and GTM teams on adoption curves and expansion dynamics.
You'll set technical standards for ML methodology across the Data organization and mentor senior data scientists and ML engineers. The role sits at the intersection of Finance, Infrastructure, Product, and GTM with high visibility across leadership and significant latitude to define how problems get solved.
Required: 8+ years in machine learning, data science, or applied statistics at staff/principal level. Deep expertise in advanced time-series forecasting, deep learning architectures, Bayesian/probabilistic modeling, and hierarchical reconciliation. Proven experience architecting and productionizing ML systems at scale. Strong Python and SQL proficiency with large-scale usage and billing datasets. Experience setting technical direction and partnering with Finance/executive leadership as a peer. Track record of technical leadership, mentoring, and influencing organizational ML approaches. Comfortable defining ambiguous, high-stakes problems autonomously in fast-moving environments.
