Staff Data Scientist, Consumption Forecasting — Hybrid

Harnham; New York (NY); On-site; USD 240,000 - 300,000; Full time

Job description

Staff Finance Data Scientist, Consumption Forecasting

Location: San Francisco or New York | Hybrid (3 days per week in office)

Salary: $240-300k base + bonus + equity (RSUs)

This is a rare chance to own forecasting infrastructure at the center of a high-growth, consumption-based developer platform, one that powers some of the world's most dynamic applications and scales with every developer and enterprise building on it.

As a consumption-based business, forecasting usage across compute, bandwidth, edge, and storage isn't a support function. It's foundational to how we plan infrastructure, revenue, and long-term strategy. This role exists to lead that work at the highest level.

What you'll own

This is a senior individual contributor role with organization-wide impact. You'll define forecasting methodology, build systems that scale with a rapidly growing platform, and sit at the intersection of Finance, Infrastructure, Product, and GTM with direct visibility to executive leadership.

• Own production revenue forecasting end-to-end: model development, backtesting, deployment, monitoring, and iteration from first principles to live system
• Build forecasting systems that account for usage-based pricing dynamics, consumption patterns, and customer lifecycle across the platform, built for how this business actually works, not retrofitted SaaS models
• Design hierarchical forecasting models across account, cohort, segment, and global aggregate levels, covering operational, quarterly, and long-range planning cycles
• Build scenario simulation frameworks to evaluate pricing changes, packaging adjustments, and product launches
• Partner with Finance on board-level reporting, with Infrastructure Engineering on capacity planning, and with Product and GTM on adoption curves and usage drivers

What we're looking for

• 7+ years in data science, quantitative analytics, or applied statistics at senior or staff level
• Deep expertise in time-series forecasting and statistical modelling in a usage-based or SaaS environment
• Proven track record building and productionizing ML systems at scale
• Strong Python and SQL, with experience on large-scale usage and billing datasets
• Familiarity with probabilistic modelling, hierarchical forecasting, and causal inference
• Experience partnering with Finance or executive leadership on planning cycles
• Comfortable operating autonomously in fast-moving, ambiguous environments

Nice to have

• Background in cloud infrastructure, developer tools, or consumption-based revenue models
• Familiarity with modern data stacks: Snowflake, Delta Lake, dbt, Airflow
• Prior technical mentorship or informal leadership experience