What's the opportunity?
As a Senior Finance Data Scientist, Existing Business, you will be the architect of the systems that predict Fin'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 Fin 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?
- 3+ years 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.