Senior Finance Data Scientist

Das ist der Job

Develop propensity models to identify expansion opportunities and churn risks before they materialize in the ledger.

Darum lohnt es sich

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.

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. About the Company 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.

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. Role Overview 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.

The Impact You Will Have Build and maintain predictive models for usage-based revenue, renewals, and expansion that outperform traditional linear forecasts. Design and maintain curated datasets that serve as the single source of truth.

Define and iterate on our LTV frameworks, providing a clear linkage between product engagement and long-term financial outcomes. Build automated, code-based forecasting workflows that increase the speed, reliability, and granularity of our financial planning. What will I be doing?

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 signals. Evaluate model performance through backtesting and iteration, ensuring our 'financial weather forecast' is constantly improving.

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. 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. #J-18808-Ljbffr