Data Scientist, Finance Forecasting

About the role

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!

Responsibilities

• 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

Requirements

• Has an advanced degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Physics, Economics) or equivalent depth through production experience
• Holds 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 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

Qualifications

• Typical starting salary for this role in the US is $215,000 - $240,000 USD
• Typical starting salary for this role in US Premium Markets is $239,000 - $267,000 USD

Skills

• Advanced degree in a quantitative discipline or equivalent production experience
• Hands-on experience building and deploying ML and statistical systems
• Deep applied statistics foundations, including time-series methods, state-space models, hierarchical approaches, or causal inference techniques
• Proficiency in Python and SQL
• Experience forecasting consumption-based or usage-billed businesses
• Bias toward action in ambiguous, early-stage environments
• Clear communication with executive stakeholders
• Fluency with AI tools and workflows
• Comfortable taking ownership of open-ended problems

Benefits

• Flexible work environment
• Healthcare
• Equity in the company
• Time off
• $500 Home office setup if you’re a remote employee
• Global Gatherings

Pay

The typical starting salary for this role in the US is $215,000 - $240,000 USD. 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.

Schedule

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.