About this role
ClickHouse is the fastest open-source analytical database in the world, processing billions of rows per second for thousands of organizations. As we scale our cloud business, the decisions that shape pricing, capacity planning, and go-to-market strategy need to be grounded in rigorous quantitative modeling, and that capability is being built from the ground up.
We're hiring a founding Data Scientist to build ClickHouse's Finance forecasting and measurement capability from the ground up. You'll own and build the forecasting models, causal measurement programs, and analytical frameworks that directly shape how leadership plans the business. You'll define the approach, build the infrastructure, and set the standard for how data science operates here.
Hybrid: We intend to fill this role in the San Francisco Bay Area, and expect this position to go into one of our Bay Area offices, Menlo Park and San Francisco, 1-2x per week.
What You'll Be Doing:
Own and build production revenue forecasting end-to-end: model development, backtesting, deployment, monitoring, and iteration
Build forecasting systems that account for the dynamics of usage-based pricing, consumption patterns, and customer lifecycle across our cloud platform
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, making forecast quality visible and continuously improving
Contribute to shared analytics infrastructure and internal tooling that accelerates data science workflows across the organization
Translate model outputs into clear, actionable recommendations for Finance, Sales, and executive leadership
Partner with Data Engineering, Revenue Operations, and Product to build the feature pipelines and data foundations your models depend on
What You Bring Along:
Has an 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 spent on forecasting or causal inference in production
Has deep applied statistics foundations, including comfort with time-series methods, state-space models, hierarchical approaches, or causal inference techniques
Is highly proficient in Python and SQL, with experience productionizing models in cloud-scale data environments
Has worked with modern analytical platforms such as ClickHouse, Snowflake, BigQuery, or Spark
Has experience forecasting consumption-based or usage-billed businesses (cloud, API, marketplace)
Has a bias toward action in ambiguous, early-stage environments and is comfortable defining the problem, not just solving it
Communicates clearly with executive stakeholders and can translate complex modeling work into actionable business recommendations
Is fluent with AI tools and workflows, including LLMs and AI coding assistants, and applies them effectively in analytical work
Is comfortable taking ownership of open-ended problems and building new functions from scratch