Senior Finance Data Scientist

Fin is the AI Customer Agent company on a mission to help businesses provide perfect customer experiences. 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. 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; Unlock predictive insights: develop propensity models to identify expansion opportunities and churn risks; Architect finance data: design and maintain curated datasets that serve as the single source of truth; Model customer value: define and iterate on LTV frameworks; Drive scalability: build automated, code-based forecasting workflows; Build and own probabilistic and time-series models that project ARR performance across renewals and usage-based motions; Incorporate behavioral signals into expansion propensity and LTV frameworks; Evaluate model performance through backtesting and iteration; Own the end-to-end data pipeline for finance; Write and optimize production-quality SQL and Python; Ensure data integrity and consistency across predictive systems and dashboards; Translate ambiguous business questions into structured data science projects; Perform scenario modeling and sensitivity analysis; Partner with Sales, Product, and Data Engineering; Translate complex statistical outputs into clear narratives for CFO and executive leadership; Build executive-ready materials

Requirements

3+ years in Data Science, Strategic Finance, or Revenue Analytics, with a deep focus on SaaS or usage-based business models; High proficiency in Python (pandas, scikit-learn) and Expert-level SQL; Experience building scalable data pipelines and production-grade analytical tools; Strong understanding of NRR, LTV, Churn, and the relationship between product usage and revenue; Ability to translate technical work into business insight and influence stakeholders through data-driven storytelling; A focus on accuracy and a Product Sense that allows you to see the human behavior behind the data points; Proficiency in leveraging AI-native development tools (e.g. Cursor, Claude Code)

Nice to have

Experience with forecasting libraries (e.g., Prophet, Nixtla)