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Salary: USD 239,000 - 267,000 / annual
ClickHouse, a Forbes Cloud 100 company and leader in real-time analytics and data warehousing, is hiring a founding Data Scientist to build its Finance forecasting and measurement capability from the ground up. This is a high-impact individual contributor role where you'll own end-to-end revenue forecasting, causal measurement frameworks, and analytical infrastructure that directly informs executive decision-making on pricing, capacity planning, and go-to-market strategy.
You will own and build production revenue forecasting systems end-to-end, including model development, backtesting, deployment, monitoring, and continuous iteration. You'll design forecasting systems that account for usage-based pricing dynamics, consumption patterns, and customer lifecycle across ClickHouse's cloud platform. A key responsibility is establishing causal measurement frameworks to quantify the revenue impact of product launches, pricing changes, and GTM initiatives.
You'll establish backtesting discipline and accuracy tracking as standing Finance metrics, making forecast quality visible and continuously improving. You'll contribute to shared analytics infrastructure and internal tooling that accelerates data science workflows across the organization, and translate complex model outputs into clear, actionable recommendations for Finance, Sales, and executive leadership.
You'll partner closely with Data Engineering, Revenue Operations, and Product teams to build the feature pipelines and data foundations your models depend on.
Required qualifications include an advanced degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Physics, Economics) or equivalent production experience. You need hands-on experience building and deploying ML and statistical systems with meaningful time spent on forecasting or causal inference in production. Deep applied statistics foundations are essential, including comfort with time-series methods, state-space models, hierarchical approaches, or causal inference techniques.
You must be 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 is required. Ideally, you have forecasted consumption-based or usage-billed businesses (cloud, API, marketplace). You should have a bias toward action in ambiguous, early-stage environments and be comfortable defining problems, not just solving them. Strong communication skills with executive stakeholders and ability to translate complex modeling work into business recommendations are essential. Fluency with AI tools and workflows, including LLMs and AI coding assistants, is expected. You should be comfortable taking ownership of open-ended problems and building new functions from scratch.
