Director, Data Science

About Northbeam

Northbeam is building the world’s most advanced marketing intelligence platform, providing top eCommerce brands a unified view of their business data through powerful attribution modeling and customizable dashboards. Our technology helps customers accurately track ad spend, understand the full customer journey, and drive profitable growth.

We’re experiencing rapid growth, have strong product-market fit, and are looking for the right people to help us scale. This is a rare chance to make a meaningful impact at a fast-moving, high-growth company. At Northbeam, you’ll join a team of driven, collaborative, and talented individuals who value personal growth and excellence. We’d love for you to be part of our journey.

We’re a remote-friendly company with offices in San Francisco and Los Angeles.

About the Data Science Organization

Northbeam’s Data Science organization serves as the intelligence layer of the company, owning the end-to-end science behind marketing measurement and optimization. This includes developing and advancing our core methodologies—Media Mix Modeling (MMM), Incrementality testing, causal multi-touch attribution (MTA), calibration and validation frameworks, and large-scale experimentation systems. The team is also responsible for AI-driven insights, recommendations, and the creation of reusable, scalable, and rigorously validated scientific systems that power Northbeam’s product and customer value.

About the Role

The Director of Data Science leads the parallel execution of complex technical initiatives across the organization, ensuring excellence in delivery, scientific rigor, and cross-functional alignment. This role oversees the development and evolution of MMM, Incrementality, Insights, and Recommendation engines. In partnership with the VP of data science, the Director will help define and shape the organization for long-term scale—setting strategic priorities, designing team structure and operating practices, and guiding resource allocation to maximize impact.

This is a hands-on leadership role focused on executing the technical roadmap: guiding technical decisions, ensuring project delivery and accountability, and coaching and unblocking the team. You’ll partner closely with Engineering, Product and Design to shape technical design and product requirements, and collaborate with R&D leadership to build the team and systems that drive continuous innovation and measurement accuracy.

Your Impact

• Own and execute the technical roadmap for data science models and scalable, production-grade data products.
• Lead cross-functional initiatives end-to-end—from problem definition through launch—driving clear milestones, accountability, and high quality standards.
• Act as a true peer partner to Engineering, Product and Design, aligning on technical strategy, architectural decisions, and long-term roadmaps.
• Lead teams responsible for developing, validating, and productionizing models, working closely with Engineering to ensure the right data sources, pipelines, and APIs are in place.
• Hire, mentor, and develop top talent while continuously raising the bar through rigorous reviews, coaching, and feedback.
• Deeply understand customer pain points and desired outcomes; turn them into clear technical requirements and DS solution designs.
• Recommend and translate statistical outputs and data science models into customer-facing features and enterprise solutions.
• Establish repeatable validation processes for model release and collaborate with Data Engineering to ensure data quality and robust edge-case handling.

What You Bring

• 8+ years in data science or ML engineering roles, including 5+ years managing senior data scientists or technical leads.
• Demonstrated ability to hire, develop, and retain senior technical talent, while building and sustaining a high-performing, inclusive team culture.
• Master’s degree (or equivalent experience) in statistics, econometrics, machine learning, or a related quantitative field.
• Strong foundation in causal inference, time series analysis, Bayesian modeling, and experimental design.
• Experience designing and deploying ML systems at scale in collaboration with engineering teams.
• Proficiency in Python, R, and SQL; experience with modern data platforms (e.g., BigQuery, Snowflake), MLOps practices, and pipeline orchestration tools.
• Strong communicati