Senior Finance Data Scientist, Existing Business

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?

As a Senior Finance Data Scientist, Existing Business, you will be the architect of the systems that predict Intercom’s revenue future. You will move beyond static reporting to build production-grade forecasting models that translate complex customer behaviors into financial signals.

You will work on high-impact, open-ended problems, such as predicting expansion propensity and modeling long-term customer LTV. This role requires a hybrid of financial intuition and technical rigor: the ability to navigate raw data warehouses and the strategic mindset to explain the "why" behind the numbers to our leadership team.

The Impact You Will Have

Own and Evolve the Revenue Engine: Build and maintain predictive models for usage-based revenue, renewals, and expansion that outperform traditional linear forecasts.

Unlock Predictive Insights: Develop propensity models to identify expansion opportunities and churn risks before they materialize in the ledger.

Architect Finance Data: Design and maintain curated datasets that serve as the single source of truth.

Model Customer Value: Define and iterate on our LTV frameworks, providing a clear linkage between product engagement and long-term financial outcomes.

Drive Scalability: Build automated, code-based forecasting workflows that increase the speed, reliability, and granularity of our financial planning.

What will I be doing?

Predictive Modeling and Forecasting Systems

Build and own probabilistic and time-series models that project ARR performance across renewals and usage-based motions.

Incorporate behavioral signals, such as product adoption, seat utilization, and feature engagement, into expansion propensity and LTV frameworks.

Design models that account for cohort dynamics, seasonality, and product-led growth (PLG) signals.

Evaluate model performance through backtesting and iteration, ensuring our "financial weather forecast" is constantly improving.

Data and Analytical Infrastructure

Own the end-to-end data pipeline for finance, transforming raw product usage and billing data into curated, model-ready datasets in our data warehouse.

Write and optimize production-quality SQL and Python to work with large-scale datasets and automate complex FP&A workflows.

Ensure data integrity and consistency across all predictive systems and executive dashboards.

Contribute to the long-term data strategy for how Intercom tracks and predicts Existing Business health.

Analytical Problem Solving

Translate ambiguous business questions (e.g., "Which usage signals best predict a 2x expansion?") into structured data science projects.

Connect ARR outcomes to underlying drivers like product adoption, customer health scores, and GTM activity.

Perform scenario modeling and sensitivity analysis to help the business understand the range of possible outcomes for NRR.

Business Partnership & Communication

Partner with Sales, Product, and Data Engineering to align our financial models with actual customer behavior and product roadmaps.

Translate complex statistical outputs into clear, decision-oriented narratives for the CFO and executive leadership.

Build executive-read