About the job
At Intercom, we are revolutionizing customer service through AI, empowering businesses to enhance their customer interactions and experiences.
Our flagship AI agent, Fin, stands at the forefront of customer service technology, enabling businesses to provide seamless, 24/7 support. Fin integrates with our Helpdesk to create the Intercom Customer Service Suite, a comprehensive solution for handling complex inquiries that require a human touch.
Since our inception in 2011, we have gained the trust of nearly 30,000 businesses worldwide, setting new benchmarks for customer service excellence. Our core values drive us to push boundaries, innovate rapidly, and deliver exceptional value to our partners.
What’s the Opportunity?
The Research, Analytics & Data Science (RAD) team is dedicated to transforming insights into actionable strategies. We discover vital customer, product, and business insights, embedding them into tools and decision systems that enhance go-to-market workflows.
With AI unlocking a new era of internal tools, we are moving beyond traditional dashboards to develop LLM and agent-powered workflows that autonomously conduct account research, summarize past interactions, generate personalized outreach, assess renewal risks, and create essential documents, allowing Sales and Success teams to concentrate on high-impact conversations.
The RAD team collaborates closely with GTM Systems to identify challenges, devise solutions, and measure our impact from inception to execution.
This role is ideal for a proactive individual who thrives in dynamic environments, relishes solving intricate real-world challenges, and is motivated by the tangible business results of their work.
What Will I Be Doing?
- Design, implement, and launch AI-driven internal tools tailored for GTM applications, such as account research, action recommendations, renewal probabilities, pipeline risk identification, QBR/autobrief generation, and post-call follow-ups.
- Manage the end-to-end process: Oversee the complete lifecycle from defining the problem and modeling data to developing production-ready solutions, including building Python backends and React frontends.
- Prototype swiftly, deploy to learn: Rapidly iterate with users, swiftly transitioning to production to enhance impact.
- Measure for success and impact: Establish metrics based on real usage and demonstrable business outcomes.
