About the job
At Intercom, we are pioneering AI-driven customer service solutions that empower businesses to deliver exceptional customer experiences.
Our flagship AI agent, Fin, represents the cutting edge of customer service technology, enabling businesses to provide continuous, high-quality service that significantly enhances customer satisfaction. When paired with our Helpdesk, Fin evolves into the Intercom Customer Service Suite, offering AI-enhanced support for complex inquiries that necessitate human intervention.
Founded in 2011, Intercom is trusted by nearly 30,000 global companies and is reshaping the standard for customer service through our relentless commitment to innovation, speed, and delivering outstanding value to our clients.
What is the opportunity?
Join our AI Group at Intercom, where we define and develop new machine learning functionalities, investigate the latest algorithms and technologies, and quickly prototype solutions to provide to our customers.
Our highly focused product team, comprising over 50 ML scientists, engineers, designers, and researchers, collaborates closely with various departments across the organization. We prioritize rapid production cycles, often transitioning from successful offline tests to beta releases in just weeks.
We continuously run experiments to assess the effectiveness of our AI features by employing both frequentist and Bayesian methodologies. We create dashboards to monitor outcomes and engage in detailed analysis of user interactions to uncover complex patterns. Working with stochastic AI products, we navigate intricate effects to drive improvements.
We are seeking a passionate Analytics Engineer to become a vital part of our AI Group, assisting us in achieving these ambitious objectives.
What will I be doing?
- Data Platform Development: Design, implement, and oversee scalable data pipelines and ETL processes to establish a robust, analytics-ready data platform.
- Cross-functional Collaboration: Collaborate with AI analysts, ML scientists, engineers, and business teams to understand data requirements and ensure reliable and user-friendly data solutions.
- Data Strategy & Governance: Spearhead initiatives in data model development, data quality stewardship, warehouse management, and production support for essential workflows.
- Advanced Analytics & Insights: Conduct in-depth data analyses and develop custom models to inform strategic business decisions and performance metrics.
- Automation & Optimization: Enhance data collection and reporting processes through automation and optimization techniques.

