About the Role

We're seeking a Staff Machine Learning Data Scientist to lead consumption forecasting at Vercel. This is a staff-level technical leadership role: you'll architect the ML systems and modeling approach behind forecasting that powers financial planning, infrastructure investment, and executive decision-making, and set the technical direction other data scientists and engineers build against.

You'll define the company's forecasting methodology from first principles, push the underlying modeling techniques well beyond off-the-shelf approaches, and build ML systems that scale with Vercel's rapidly growing platform. The role sits at the intersection of Finance, Infrastructure, Product, and GTM, with high visibility across leadership and significant latitude to define how the problem gets solved.

What You Will Do

• Architect and own Vercel's end-to-end consumption forecasting ML systems across compute, bandwidth, edge functions, storage, and emerging products.
• Design and productionize advanced ML approaches for time-series forecasting (deep learning-based forecasting, probabilistic/Bayesian methods, hierarchical and hybrid statistical-ML architectures), going beyond standard forecasting libraries where the problem demands it.
• Develop multi-horizon forecasting systems, from operational to quarterly to long-range planning, including hierarchical architectures that reconcile predictions across account, cohort, segment, and global aggregate levels.
• Build the ML infrastructure and tooling for backtesting, monitoring, drift detection, and forecast explainability, setting the standard other data scientists build on.
• Develop scenario simulation and causal inference frameworks to evaluate pricing changes, packaging adjustments, and product launches before they ship.
• Partner directly with Finance leadership on board-level reporting and revenue planning, and with Infrastructure Engineering on capacity planning and cost optimization, acting as the technical authority on what the models can and can't tell them.
• Work with Product and GTM teams to model adoption curves, expansion dynamics, and usage drivers using advanced causal and predictive techniques.
• Set technical standards for ML methodology, experimentation, and measurement across the Data organization, and mentor senior data scientists and ML engineers.