Staff Finance Data Scientist, Consumption Forecasting

Harnham; San Francisco (CA); Hybrid; USD 240,000 - 300,000; Full time

Benefits offered by this job

Bonus

Equity (RSUs)

Job summary

Harnham is looking for a Staff Finance Data Scientist specializing in Consumption Forecasting to join their team. This role, based in San Francisco or New York, will leverage your expertise in data science and forecasting to build scalable systems impacting organizational strategy.

You will own production revenue forecasting, partner with key stakeholders, and drive forecasting methodologies for a growing platform.

We require deep expertise with time-series forecasting, strong Python and SQL skills, and a minimum of 7 years experience.

Qualifications

• 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.

Responsibilities

• Own production revenue forecasting end-to-end.
• Build forecasting systems that account for usage-based pricing dynamics.
• Design hierarchical forecasting models across account, cohort, segment, and global aggregate levels.

Skills

Time-series forecasting

Statistical modelling

Python

SQL

Machine learning systems

Quantitative analytics

Tools

Snowflake

Delta Lake

dbt

Airflow

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