Senior Data Scientist - AI Tooling

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's the opportunity? The Research, Analytics & Data Science (RAD) team turns insight into action. We uncover customer, product, and business insights and translate them into tools and decision systems embedded directly into GTM workflows. AI has unlocked an entirely new generation of internal tools for our GTM teams. We’re evolving from static dashboards to LLM and agent-powered workflows that do the work: auto-researching accounts, summarizing prior interactions, drafting personalized outreach, flagging renewal risk, and assembling decks and docs - enabling Sales and Success to focus on high-value conversations. The RAD team partners closely with GTM Systems to define problems, solutions, and measure impact - from prototype to production. This is a high-ownership role for someone who thrives in ambiguity, is energized by solving complex real-world problems, and is motivated by seeing their work translate into tangible business impact. What will I be doing? Design, evaluate, and ship AI-powered internal tools for GTM use cases - including account research & summaries, next-best-action recommendations, renewal propensity, pipeline risk detection, QBR/autobrief generation, and post-call summarization & follow-ups. Work end-to-end: Own the full lifecycle, from problem definition and data modeling to building production-ready tools, including writing Python backends and React frontends. Prototype fast, ship to learn: Rapidly build with users, then productionize quickly to iterate and deliver impact. Instrument for adoption and outcomes: Define success through real usage and measurable business impact (e.g., improved win rate, conversion, expansion). Evangelize and enable: Document playbooks, run enablement sessions, and help leaders operationalize new tooling across teams. What skills do I need? Proven track record of applied data science with measurable GTM

Required Skills