Data Scientist

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.

We are seeking a Staff Data Scientist to advance the statistical and experimentation foundations of Northbeam’s marketing intelligence products, including MMM, Incrementality, Insights, and Recommendation engines. This is a full-stack data scientist role, operating across the entire lifecycle—from researching and developing advanced statistical and causal inference approaches to implementing, deploying, and maintaining production systems.

You will work at the intersection of R&D and product, owning modeling end-to-end: from data exploration and experimental design to model validation, production integration, and live system iteration. You will bring a deep understanding of statistical modeling techniques and, critically, the assumptions underlying them. This role requires strong judgment in selecting, stress‑testing, and adapting models to real‑world marketing data where ideal conditions rarely exist.

This is a high‑agency, hands‑on role, requiring comfort shifting between deep technical work, cross‑functional collaboration, and customer‑facing problem solving as business needs evolve.

• Develop and evolve statistical and causal inference methodologies that strengthen the rigor and reliability of Northbeam’s marketing measurement products.
• Enhance the accuracy, robustness, and scalability of existing MMM pipelines.
• Write production‑quality Python and SQL, collaborating closely with Engineering to integrate statistical workflows into scalable systems.
• Rigorously assess model assumptions, identify potential sources of bias, and implement mitigation strategies.
• Debug data and modeling issues in production and improve the reliability of customer‑facing results.
• Translate complex statistical reasoning into clear, decision‑ready insights for internal stakeholders and customers.

• Bachelor’s degree in Mathematics, Computer Science, or a highly quantitative STEM field (Master’s preferred).
• 6+ years of applied experience in data science, machine learning, or statistical modeling roles.
• Deep applied experience in statistics and causal inference, including hands‑on ownership of experiment design (A/B testing, geo‑based experiments, or quasi‑experimental methods), power analysis, and interpretation of lift.
• Strong understanding of regression, time series modeling, Bayesian methods, and model evaluation in real‑world business settings.
• Experience translating statistical research or experimentation frameworks into production‑ready systems.
• Proficiency in Python and SQL, with experience working on modern data platforms and statistical modeling libraries.
• Experience working with production code and comfort making changes to live models and pipelines.
• Interest in (or prior experience with) marketing measurement, including MMM and incrementality, with a strong desire to deepen domain expertise.
• Thrives in a small, fast‑paced environment, with the ability to drive projects end‑to‑end.
• Strong communication skills, with comfort explaining technical concepts to non‑technical audiences and supporting customer‑facing investigations.
• A growth mindset and genuine curiosity about improving measurement quality and decision‑making.

• Experience in marketing attribution, MMM, incrementality, or related marketing measurement challenges.
• Experience with optimization techniques (e.g., linear programming, mixed‑integer programming) applied in production systems.
• Familiarity with MLOps practices and data pipeline orchestration tools.
• Startup Experiences.
• PhD in a quantitative field.

Base Salary Range: $180,000—$230,000 USD

Actual compensation may vary based on experience, skills, and location.

In addition to your base salary, we offer an equity package, comprehensive healthcare benefits (medical, dental, and vision), and a 401(k) plan. Our team enjoys a flexible PTO policy, 12 company‑paid holidays, and 12 weeks of paid parental leave. We also provide a $500 work‑from‑home stipend to support your remote setup.