Senior Analytics Engineer

Job Description

Intercom is the AI Customer Service company on a mission to help businesses provide incredible customer experiences. Our AI agent Fin, the most advanced customer service AI agent on the market, lets businesses deliver always-on, impeccable customer service and ultimately transform their customer experiences for the better. Fin can also be combined with our Helpdesk to become a complete solution called the Intercom Customer Service Suite, which provides AI enhanced support for the more complex or high touch queries that require a human agent. Founded in 2011 and trusted by nearly 30,000 global businesses, Intercom is setting the new standard for customer service. Driven by our core values, we push boundaries, build with speed and intensity, and consistently deliver incredible value to our customers. What is the opportunity? Intercom’s AI Group is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers’ hands. We are extremely product-focussed. Our team of 50+ ML scientists, ML engineers, designers and researchers works in partnership with other teams across the whole company. We move to production fast, often shipping to beta within weeks of a successful offline test. We constantly run experiments and measure the success of our AI features. We use frequentist and Bayesian approaches. We create dashboards to track results. We dive deep into exactly how users are being successful, and have to tease out all sorts of complex user interactions. We have to deal with inherently stochastic AI products, and complex effects. We are looking for a dedicated Analytics Engineer to join the AI Group to help us with that. What will I be doing? Data Platform Development: Design, build, and manage scalable data pipelines and ETL processes to support a robust, analytics-ready data platform. Cross-functional Collaboration: Partner with ai analysts, ml scientists, engineers and business teams to understand data needs and ensure accurate, reliable & ergonomic data solutions. Data Strategy & Governance: Lead initiatives in data model development, data quality ownership, warehouse management, and production support for critical workflows. Advanced Analytics & Insights: Conducted data analysis and built custom models to support strategic business decisions and performance measurement. Automation & Optimization: Streamline data collection and reporting processes to reduce manual effort and improve efficiency. Innovation in Data Infrastructure: Created scalable solutions like unified data pipelines and access control systems to meet evolving organizational needs. Strategic Influence: Work

Required Skills