Data Scientist, Finance Forecasting

Stack

Responsibilities

• ▹Own production revenue forecasting end-to-end: model development, backtesting, deployment, monitoring, iteration
• ▹Build forecasting systems accounting for usage-based pricing, consumption patterns, and customer lifecycle dynamics
• ▹Design and implement causal measurement frameworks to quantify the revenue impact of product launches, pricing changes, and GTM motions
• ▹Establish backtesting discipline and accuracy tracking as standing Finance metrics
• ▹Contribute to shared analytics infrastructure and internal tooling
• ▹Translate model outputs into clear, actionable recommendations for Finance, Sales, and executive leadership
• ▹Partner with Data Engineering, Revenue Operations, and Product to build required data pipelines

Requirements

• ▹Advanced degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Physics, Economics) or equivalent depth through production experience
• ▹Hands-on experience building and deploying ML and statistical systems, with meaningful time on forecasting or causal inference in production
• ▹Deep applied statistics foundations: time-series methods, state-space models, hierarchical approaches, or causal inference techniques
• ▹Highly proficient in Python and SQL, with experience productionizing models in cloud-scale data environments
• ▹Experience with modern analytical platforms such as ClickHouse, Snowflake, BigQuery, or Spark
• ▹Experience forecasting consumption-based or usage-billed businesses (cloud, API, marketplace)
• ▹Bias toward action in ambiguous, early-stage environments
• ▹Clear communication with executive stakeholders, translating complex modeling into business recommendations
• ▹Fluent with AI tools and workflows, including LLMs and AI coding assistants

Soft skills

About the company

ClickHouse is the fastest open-source analytical database in the world, processing billions of rows per second for thousands of organizations; this hybrid role is based out of the Bay Area (Menlo Park / San Francisco), 1-2 days per week.