Senior Finance Data Scientist

Fin is the AI Customer Agent company on a mission to help businesses provide perfect customer experiences. Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey – from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk for one single system that is designed to meet the needs of modern day support teams. Founded in 2011, Fin became one of the fastest growing companies and remains one of the largest private software companies in the world with nearly 30,000 global businesses using our products to transform their customer support. Driven by our core values, we push boundaries, build with speed and intensity, and relentlessly 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 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 stakeholder

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 stakeholder

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 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 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.