Staff Applied Scientist, Financial Forecasting

Vercel • San Francisco, CA

4d • $250,000 - $330,000 • Remote

About The Position

Vercel is seeking a Staff Machine Learning Data Scientist to lead consumption forecasting. This is a staff-level technical leadership role where you will architect ML systems and modeling approaches for financial planning, infrastructure investment, and executive decision-making. You will define the company's forecasting methodology from first principles, push modeling techniques beyond off-the-shelf approaches, and build scalable ML systems for Vercel's rapidly growing platform. The role is at the intersection of Finance, Infrastructure, Product, and GTM, offering high visibility and significant autonomy.

You will architect and own Vercel's end-to-end consumption forecasting ML 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 forecasting, probabilistic/Bayesian methods, and hierarchical/hybrid statistical-ML architectures. You will develop multi-horizon forecasting systems for operational to long-range planning, including hierarchical architectures that reconcile predictions across different levels. You will build ML infrastructure for backtesting, monitoring, drift detection, and forecast explainability. Additionally, you will develop scenario simulation and causal inference frameworks to evaluate changes before they ship. You will partner directly with Finance leadership on board-level reporting and revenue planning, and with Infrastructure Engineering on capacity planning and cost optimization. You will also work with Product and GTM teams to model adoption curves, expansion dynamics, and usage drivers. Finally, you will set technical standards for ML methodology, experimentation, and measurement, and mentor senior data scientists and ML engineers.

Requirements

• 8+ years of experience in machine learning, data science, or applied statistics, with a track record of operating at a staff or principal level.
• Deep, hands-on expertise in advanced time-series forecasting and ML modeling techniques (deep learning architectures for forecasting, Bayesian/probabilistic modeling, hierarchical reconciliation), not just applied statistics.
• Proven experience architecting and productionizing ML systems at scale, including the infrastructure for training, serving, monitoring, and retraining models in production.
• Strong Python and SQL proficiency, with deep experience on large-scale usage and billing datasets, plus a strong grounding in causal inference and experimentation design.
• Experience setting technical direction and partnering closely with Finance or executive leadership on planning cycles, as a peer to senior stakeholders, with the ability to translate advanced ML concepts into decision-ready insights for non-technical audiences.
• A track record of technical leadership: setting standards, mentoring senior ICs, and influencing how an organization approaches ML and forecasting.
• Comfortable defining ambiguous, high-stakes problems from scratch and operating autonomously in a fast-moving environment.
• Experience in cloud infrastructure, developer tools, or consumption-based revenue models.

Nice To Haves

• Background in capacity planning or cost modeling at scale.
• Experience with modern data and ML stacks (e.g., Snowflake, Delta Lake, dbt, Airflow, feature stores, MLOps tooling).
• Prior experience as a technical lead for a data science or ML team, even without formal management authority.

Responsibilities

• 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.

Benefits

• Competitive compensation package, including equity.
• Inclusive Healthcare Package.
• Learn and Grow - we provide mentorship and send you to events that help you build your network and skills.
• Flexible Time Off.
• We will provide you the gear you need to do your role, and a WFH budget for you to outfit your space as needed.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed