Northbeam

Social Media Manager

• Publish engaging daily/weekly content for social platforms for Northbeam (LinkedIn, Instagram, X/Twitter, TikTok, Facebook, YouTube, etc.).
• Collaborate with designers, copywriters, and marketing leads to ensure consistent brand messaging and tone.
• Maintain a social media content calendar aligned with product launches, campaigns, and company events.
• Write compelling copy and curate visual assets that drive engagement and conversions.
• Monitor comments, messages, and mentions across social channels, responding promptly and professionally.
• Foster relationships with followers, customers, influencers, and brand advocates.
• Track relevant industry trends, conversations, and competitor activities.
• Measure key social metrics (reach, engagement rate, click-throughs, follower growth, etc.).
• Prepare performance reports and insights for the marketing team.
• Suggest improvements based on data and audience feedback.
• Assist with social campaigns, including asset management and performance tracking.
• Support influencer partnerships and UGC (user-generated content) initiatives.
• Collaborate on cross-channel marketing efforts (email, PR, paid media, etc.)

Social Media Coordinator

Backend Engineer

• Design and implement scalable, high-performance data pipelines to ingest and transform data from a variety of sources, ensuring reliability, observability, and maintainability.
• Build and maintain APIs that enable flexible, secure, and tenant-aware data integrations with external systems.
• Work with event-driven and batch processing architectures, ensuring data freshness and consistency at scale.
• Drive clean API design and integration patterns that support both real-time and batch ingestion while handling diverse authentication mechanisms (OAuth, API keys, etc.).
• Implement observability, monitoring, and alerting to track data freshness, failures, and performance issues, ensuring transparency and reliability.
• Optimize data flows and transformations, balancing cost, efficiency, and rapid development cycles in a cloud-native environment.
• Collaborate with data engineering, infrastructure, and product teams to create an integration platform that is flexible, extensible, and easy to onboard new sources.

Senior Software Engineer, Data Systems – Python

• Work with our customers, product managers, product engineers, and customer support, to scope, build, and iterate high value solutions.
• Design, build and optimize data ingestion and transformation pathways spanning APIs, file processing, and configurable inputs.
• Maintain and enhance the data/infrastructure engineering aspects end-to-end ensuring high performance, scalability and reliability
• Work on the data pipeline architecture and optimize it for readability, maintainability, as well as cost.
• Integrate with our machine learning and data science systems to deliver insights to our customers.
• Maintain and enhance the necessary infrastructure to scale our platform.
• Participate in engineering on-call rotations, addressing production issues and diving into both code and data to analyze and solve problems reported through customer-facing channels.
• Write technical documentation for internal and external stakeholders.

Software Engineer, Data Systems – Python

Customer Success Manager

• Lead and manage the Customer Success Manager (CSM) team, providing mentorship, coaching, and performance management.
• Define and evolve the team structure, processes, and career development paths as the Customer Success function scales.
• Continue hiring CSMs and Senior CSMs to support our client base.
• Own the post-sales customer lifecycle strategy with the goal of maximizing retention and improving customer experience.
• Develop and implement proactive customer success programs to improve onboarding, product adoption, customer health, and advocacy.
• Establish clear KPIs and reporting to track customer health, team performance, and business impact.
• Build and refine scalable processes to drive operational efficiency and consistency across the CSM team.
• Partner closely with Sales, Media Strategy, Product, and Engineering to deliver a seamless customer experience and voice of customer feedback loop.
• Drive alignment between Customer Success and Media Strategy on data reviews, reporting cadences, and campaign effectiveness insights.
• Influence product roadmap priorities by collecting and synthesizing feedback from the Customer Success team and customers.
• Engage directly with strategic customers as an executive sponsor and trusted advisor; lead high-risk retention efforts and escalations.
• Represent Customer Success in executive leadership discussions and planning.
• Identify and drive opportunities to automate and enhance customer engagement, health tracking, and value realization through tooling and workflows.
• Champion continuous learning and adoption of industry best practices within the Customer Success function.

Director of Customer Success

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
• Work directly with customers and internal teams to deliver reliable marketing measurement solutions
• Flexibly shift between technical implementation and customer support as needs evolve
• Grow through mentorship from senior team members and take ownership of technical projects

• 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
• Take ownership of technical projects and receive mentorship from senior team members

Staff 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

• 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

• 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

• 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

• 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.