Why This Job is Featured on The SaaS Jobs
This role sits at the intersection of finance, product telemetry, and revenue operations, a core junction in modern SaaS where usage signals increasingly drive commercial outcomes. The remit goes beyond reporting into building forecasting systems that translate adoption, engagement, and cohort behavior into ARR, renewal, and expansion expectations. In a company operating with usage based and existing business motions, that kind of modeling becomes central to how leadership interprets performance and plans capacity.
For a SaaS career, the long term value is the chance to develop a durable toolkit around recurring revenue mechanics and the data foundations behind them. Work like probabilistic forecasting, LTV frameworks, and propensity modeling maps cleanly to common SaaS questions across PLG and sales led environments. Owning curated finance datasets and automated workflows also builds credibility in analytics engineering style practices that many SaaS organizations rely on to scale planning.
This position best suits someone who prefers open ended problems with clear business stakes and enjoys being accountable for production grade analytical systems. It will resonate with professionals who can move comfortably between SQL level detail and executive level narrative, and who want their data science work tied directly to recurring revenue decisions rather than isolated experimentation.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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-ready materials, including predictive dashboards and strategic presentations.
What skills do I need?
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- 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.
Benefits
We are a well-treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us!
- Competitive salary and equity in a fast-growing start-up
- We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen
- Regular compensation reviews - we reward great work!
- Pension scheme & match up to 4%
- Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents
- Flexible paid time off policy
- Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones
- If you’re cycling, we’ve got you covered on the Cycle-to-Work Scheme. With secure bike storage too
- MacBooks are our standard, but we also offer Windows for certain roles when needed.
#LI-Hybrid
