Staff ML Scientist: Time-Series Forecasting & Systems

vercel.com; New York, Northern (NY, KY); Hybrid; USD 250,000 - 330,000; Full time

Benefits offered by this job

Equity

Healthcare

Mentorship

Flexible time off

WFH budget

Job summary

Vercel, based in San Francisco, CA, seeks a Staff Machine Learning Data Scientist to lead consumption forecasting and architect ML systems powering financial planning and capacity decisions.

You will define forecasting methodologies, push modeling techniques beyond standard approaches, and build scalable ML systems aligned with company strategy, with high visibility across leadership.

Qualifications

• 8+ years of experience in machine learning, data science, or applied statistics at staff/principal level.
• Hands-on expertise in advanced time-series forecasting and ML modeling techniques.
• Experience architecting and productionizing ML systems at scale.
• Strong Python and SQL proficiency with large-scale datasets.
• Experience partnering with Finance or executive leadership on planning cycles.
• Proven track record in technical leadership and mentoring senior ICs.

Responsibilities

• Architect and own end-to-end forecasting ML systems across compute, bandwidth, edge, storage, and products.
• Develop advanced ML approaches for time-series forecasting beyond standard libraries.
• Build multi-horizon forecasting; reconcile predictions across levels.
• Create ML infrastructure for backtesting, monitoring, drift detection, and explainability.
• Collaborate with Finance, Infrastructure, Product, and GTM on planning and pricing scenarios.
• Set technical standards and mentor data science teams.

Skills

Time-series forecasting

Python

SQL

Causal inference

Leadership

Tools

Snowflake

Delta Lake

dbt

Airflow

MLOps tooling

Job description

Vercel, based in San Francisco, CA, seeks a Staff Machine Learning Data Scientist to lead consumption forecasting and architect ML systems powering financial planning and capacity decisions.

You will define forecasting methodologies, push modeling techniques beyond standard approaches, and build scalable ML systems aligned with company strategy, with high visibility across leadership.