Northbeam

Data Scientist

• Own and advance the statistical foundations of marketing-effectiveness measurement, ensuring methodologies are robust, reliable, and scalable
• Research, prototype, and deploy advanced attribution and AI models for marketing measurement
• Design and evolve complex data science models that balance scientific rigor with practical, scalable frameworks
• Build and maintain experimentation frameworks for model validation and lift measurement
• Define and implement validation and release criteria for attribution model and product launches, including data-quality checks, model benchmarking, and validation metrics
• Collaborate with Software Engineers and Product Managers to productionize and productize work
• Mentor Data Scientists on complex statistical and methodological challenges, including experiment design; raise the bar for individual execution via reviews and training
• Serve as technical architect for attribution methodologies, experiment design, and statistical inference; act as hands-on technical lead advancing AI-powered measurement techniques

Principal Data Scientist, Marketing Measurement

• Own and advance the statistical foundations of marketing-effectiveness measurement, ensuring methodologies are robust, reliable, and scalable.
• Research, prototype, and deploy advanced attribution and AI models for marketing measurement.
• Turn ambiguous attribution problems into reliable systems that drive confident business decisions.
• Serve as the technical architect for attribution methodologies, experiment design, and statistical inference.
• Design and evolve complex data science models that balance scientific rigor with practical, scalable frameworks.
• Build and maintain experimentation frameworks for model validation and lift measurement.
• Define and implement validation and release criteria for attribution model and product launches, including data-quality checks, model benchmarking, and validation metrics.
• Collaborate with Software Engineers and Product Managers to productionize and productize your work.
• Mentor Data Scientists on complex statistical and methodological challenges, including experiment design; raise the bar for individual execution via reviews and training.

Principal Data Scientist

• Support both new and existing data science–powered products
• Contribute to forecasting and experimentation capabilities, improving model accuracy and expanding measurement coverage
• Explain statistical concepts to customers, troubleshoot measurement issues, and gather feedback to enhance Northbeam's product suite
• Collaborate with engineering and product teams to translate customer needs into technical requirements
• Provide technical expertise and support for non-technical stakeholders, both internal and external
• Receive mentorship from senior team members and take ownership of technical projects as you grow

Senior Data Scientist

• Lead the team that builds the intelligence layer underpinning Northbeam's marketing measurement and data-driven products
• Hands-on leadership focused on executing the technical roadmap, guiding technical decisions, and ensuring project delivery and accountability
• Coach, mentor, and unblock the team; hire and develop top talent
• Partner closely with Engineering and Product to shape technical design, align milestones, and ensure execution velocity
• Collaborate with R&D leadership to build systems that drive continuous innovation and measurement accuracy
• Own execution of the technical roadmap for data science models and scalable data products
• Lead cross-functional projects end-to-end—from scoping to launch—with clear milestones and quality standards
• Translate customer pain points into 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

Director, Data Science

Backend Engineer

• Northbeam is fundamentally a data product - the whole company. We don’t sell shoes, or ads, or games. We sell data: quality integrations with a variety of platforms, fresh and reliable data pulls, robust data ingest APIs, correct aggregations, and algorithmic insights on top of that data—all packaged into a user-facing application.
• High-quality, reliable data integration is at the core of what we do, and your work will directly shape the company’s success. But building great data products also means building great infrastructure: scalable systems, resilient pipelines, and reliable platforms that empower everything else.
• We are looking for a Senior Software Engineer with experience in data integration, API-based ETL pipelines, and cloud-native architecture, plus a strong interest in infrastructure engineering. You’ll help us not just ship data pipelines, but also design the systems that make them observable, secure, and resilient at scale.
• You’ll work with a small engineering team to create a platform that consolidates third-party data from advertising platforms, e-commerce systems, customer data warehouses, ERP, POS, and CRM systems. Along the way, you’ll tackle questions of scalability, multi-tenancy, system reliability, data validation, cost optimization, and developer ergonomics.
• Curiosity, willingness to do the hard thing, and an enjoyment of a startup pace of development will be the key to success in this role.
• This is a startup. The only constant is change. Early on, you can expect to:
• Design and implement scalable, high-performance data pipelines to ingest and transform data from a variety of sources, with reliability and observability baked in.
• Engineer the infrastructure behind those pipelines, including containerized workloads, orchestration, monitoring, and CI/CD that enables the team to move quickly without breaking things.
• Build and maintain APIs that enable flexible, secure, and tenant-aware data integrations with external systems.
• Balance event-driven and batch processing architectures, ensuring data freshness, correctness, and cost efficiency.
• Implement observability, monitoring, and alerting to track system health, failures, and performance issues—covering both data quality and infrastructure reliability.
• Contribute to platform resilience by designing for fault tolerance, autoscaling, and graceful failure handling in a multi-tenant cloud environment.
• Collaborate across data engineering, infrastructure, and product teams to ensure that the integration platform is flexible, extensible, and easy to onboard new data sources.

Principal Data Scientist, Marketing Measurement

Principal Data Scientist

Senior Data Scientist

Director, Data Science

Senior Software Engineer, Python, Data & Infrastructure