Senior Data Scientist

About the Role

We are seeking a Senior Data Scientist to help build and scale Northbeam's measurement products, including MMM, Incrementality, Insights, and Recommendation Systems.

This is an applied data science role focused on translating statistical and causal inference methodologies into reliable, production-grade systems. You will work across the full lifecycle of measurement products—from methodology evaluation and model development to implementation, deployment, monitoring, and customer support.

This role emphasizes production implementation and operational ownership as much as statistical methodology.

The ideal candidate combines strong statistical foundations with a builder mindset and is comfortable moving between modeling, software development, production operations, and customer-facing problem solving.

Your Impact

• Build, deploy, and maintain production systems that power Northbeam's measurement products.
• Translate statistical and causal inference methodologies into scalable, reliable customer-facing capabilities.
• Improve the accuracy, coverage, reliability, and operational robustness of our measurement systems.
• Debug data, modeling, and production issues across the full stack, from source data to customer-facing outputs.
• Evaluate methodological improvements pragmatically, balancing statistical rigor, implementation complexity, maintainability, and business value.
• Partner closely with teammates across Data Science, Engineering, Product, and Customer Success to improve measurement quality and product capabilities.
• Explain statistical concepts to customers, troubleshoot measurement issues, and gather feedback to improve Northbeam's products.

What You Bring

• Bachelor's degree (MS or PhD preferred) in Computer Science, Statistics, Mathematics, Engineering, or another highly quantitative field.
• 5+ years of experience building and deploying data science, machine learning, or AI systems in production environments.
• Strong foundation in statistics, machine learning, experimentation, and causal inference, with experience applying these methods to solve real-world business problems.
• Strong coding and debugging skills, with the ability to write production-quality code that is maintainable, testable, and reliable.
• Experience implementing, deploying, and supporting statistical or machine learning systems in production environments.
• Ability to debug issues across the full stack, including source data, data pipelines, model logic, production services, and customer-facing outputs.
• Experience owning projects end-to-end, from problem definition and methodology evaluation through production deployment and operational support.
• Comfortable working in a fast-paced startup environment and driving projects from concept to production with a