Data Scientist, Finance Forecasting

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