# Data Scientist, Finance Forecasting

**Company:** [Clickhouse](https://hotfix.jobs/companies/clickhouse)
**Location:** Menlo Park, CA
**Role:** Data Science
**Salary:** $215k – $267k/yr
**Skills:** Python, SQL, ClickHouse, Snowflake, BigQuery, Spark, Time-Series Methods, Causal Inference, Machine Learning, LLMs
**Posted:** 2026-04-15

> Builds end-to-end revenue forecasting models and causal measurement frameworks for finance planning in a usage-based cloud business. Requires advanced quantitative degree, production ML/stats experience with forecasting/causal inference, and proficiency in Python/SQL on analytical platforms.

## Job Description

## 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

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