Staff Applied Scientist

Title: Staff Applied Scientist, Financial Forecasting

Location: Hybrid - San Francisco, New York City

About Vercel:

Vercel is the agentic infrastructure company. We free people and agents to ship what’s next.

For more than a decade, Vercel has shaped how the web is built. As the team behind Next.js, v0, and AI SDK, we create products that help builders move from idea to production with speed, security, and exceptional developer experience.

Now, software is entering a new era, and the next generation of products will not just be used by people. They will be built, extended, and operated by agents.

We are building the platform for that future, trusted by companies like OpenAI, PayPal, Ramp, Supreme, and millions of developers worldwide. Whether you’re building our products, supporting our customers, growing our community, or shaping our story, you’ll help define what comes next.

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.

About You

• 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 Have

• 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.

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.

The San Francisco, CA base pay range for this role is $250,000 - $330,000. Actual salary will be based on job-related skills, experience, and location. Compensation outside of San Francisco may be adjusted based on employee location. The total compensation package may include benefits, equity-based compensation, and eligibility for a company bonus or variable pay program depending on the role. Your recruiter can share more details during the hiring process. 

Vercel is committed to fostering and empowering an inclusive community within our organization. We do not discriminate on the basis of race, religion, color, gender expression or identity, sexual orientation, national origin, citizenship, age, marital status, veteran status, disability status, or any other characteristic protected by law. Vercel encourages everyone to apply for our available positions, even if they don't necessarily check every box on the job description.