Data Scientist, Finance Forecasting at Clickhouse

About ClickHouse Recognized on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies. With more than 3,000 customers and ARR that has grown over 250 percent year over year, ClickHouse leads the market in real-time analytics, data warehousing, observability, and AI workloads. The company’s sustained, accelerating momentum was recently validated by a $400M Series D financing round. Over the past three months, customers including Capital One, Lovable, Decagon, Polymarket, and Airwallex have adopted the platform or expanded existing deployments. These customers join an established base of AI innovators and global brands such as Meta, Cursor, Sony, and Tesla. We’re on a mission to transform how companies use data. Come be a part of our journey! 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 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 our Menlo Park office 1-2x per week. What You'll Be Doing: • Own 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 The typical starting salary for this role in the US is$215,000—$240,000 USDThe typical starting salary for this role in US Premium Markets is$239,000—$267,000 USD Compensation For roles based in the United States, the typical starting salary range for this position is listed above. In certain locations, such as the San Francisco Bay Area and the New York City Metro Area, a premium market range may apply, as listed. These salary ranges reflect what we reasonably and in good faith believe to be the minimum and maximum pay for this role at the time of posting. The actual compensation may be higher or lower than the amounts listed, and the ranges may be subject to future adjustments. An individual’s placement within the range will depend on various factors, including (but not limited to) education, qualifications, certifications, experience, skills, location, performance, and the needs of the business or organization. If you have any questions or comments about compensation as a candidate, please get in touch with us at [Upgrade to PRO to see contact]. Perks • Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 20 countries. • Healthcare - Employer contributions towards your healthcare. • Equity in the company - Every new team member who joins our company receives stock options. • Time off - Flexible time off in the US, generous entitlement in other countries. • A $500 Home office setup if you’re a remote employee. • Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. Culture - We All Shape It As part of a rapidly scaling start up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here. Check out our blog posts or follow us on LinkedIn to find out more about what’s happening at ClickHouse. Equal Opportunity & Privacy ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here for our Privacy Statement.