Senior Finance Data Scientist, Existing Business

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.

Responsibilities

• 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.
• 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.
• Analytical Problem Solving: Translate ambiguous business questions into structured data science projects.
• Business Partnership & Communication: Partner with Sales, Product, and Data Engineering to align our financial models with actual customer behavior and product roadmaps.

What skills do I need?

• 5 to 8 years of experience in Data Science, Strategic Finance, or Revenue Analytics, with a deep focus on SaaS or usage-based business models.
• Advanced Technical Skills: High proficiency in Python (pandas, scikit-learn) and Expert-level SQL. Experience with forecasting libraries (e.g., Prophet, Nixtla) is a major plus.
• System Design Mindset: Experience building scalable data pipelines and production-grade analytical tools, not just one-off spreadsheets.
• SaaS Mastery: Strong understanding of NRR, LTV, Churn, and the relationship between product usage and revenue.
• Communication: Ability to translate technical work into business insight and influence stakeholders through data-driven storytelling.
• Business Judgment: A focus on accuracy and a "Product Sense" that allows you to see the human behavior behind the data points.
• AI-Augmented Productivity: Proficiency in leveraging AI-native development tools (e.g. Cursor, Claude Code) to accelerate the development of data pipelines, model prototyping, and code documentation.

What Success Looks Like

• Automated forecasting models that are more accurate, granular, and less manual than previous iterations.
• A clear Propensity Score integrated into our planning that successfully predicts customer expansion and contraction.
• Scalable, code-based workflows that reduce the time-to-insight for the Existing Business team.
• High confidence from leadership in our ability to predict the financial impact of changing customer usage patterns.