Senior Finance Data Scientist

About Fin

Fin is a leading AI Customer Agent company dedicated to enabling businesses to deliver exceptional customer experiences. Our proprietary AI Agent powers unrivaled customer support across multiple channels—service, sales, and ecommerce—providing end-to-end resolution with minimal integration. Founded in 2011, Fin has become one of the largest private software firms globally, with nearly 30,000 customers transforming their support through our solutions. We prioritize pushing boundaries, building rapidly, and delivering outstanding customer value.

Role Overview

As a Senior Finance Data Scientist focused on Existing Business, you will design and own predictive systems that forecast Fin’s revenue trajectory. Moving beyond static reports, you will create advanced forecasting models that interpret complex customer behaviors into financial insights to guide leadership decisions. This role requires a blend of financial understanding and rigorous technical expertise.

Key Responsibilities

• Develop and maintain advanced predictive models for usage-based revenue, renewals, and expansion, consistently outperforming traditional linear forecasts.
• Create propensity models to detect expansion opportunities and churn risks proactively.
• Design and maintain authoritative financial datasets that serve as a single source of truth.
• Define and refine long-term customer Lifetime Value (LTV) frameworks linking product engagement with financial outcomes.
• Build automated, scalable code-based forecasting workflows to improve speed, accuracy, and detail in financial planning.

Technical Responsibilities

• Construct probabilistic and time-series models predicting Annual Recurring Revenue (ARR) performance across renewals and usage drivers.
• Integrate behavioral indicators such as product adoption, seat usage, and feature interaction into forecasting models.
• Design models accounting for cohort changes, seasonality, and product-led growth signals.
• Assess and iterate model accuracy through backtesting to continuously improve financial forecasts.
• Manage end-to-end financial data pipelines, transforming raw usage and billing data into curated, model-ready datasets.
• Write and optimize production-level SQL and Python scripts to handle large datasets and automate complex financial workflows.
• Guarantee data consistency and integrity across predictive models and executive reporting dashboards.
• Contribute to strategic data approaches for tracking and forecasting the health of existing business revenue.

Analytical & Collaborative Tasks

• Translate broad business questions into well-defined data science projects.
• Connect revenue metrics to underlying drivers including product adoption and customer engagement.
• Conduct scenario analyses and sensitivity tests to forecast the range of possible Net Revenue Retention outcomes.
• Collaborate closely with Sales, Product, and Data Engineering teams to align financial models with real-world customer and product data.
• Communicate complex statistical findings as clear, actionable insights to executives and stakeholders.
• Prepare executive-level predictive dashboards and strategic presentations.

Required Qualifications & Skills

• Minimum of 3 years’ experience in Data Science, Strategic Finance, or Revenue Analytics focused on SaaS or usage-based models.
• Expert proficiency in Python (especially pandas, scikit-learn) and advanced SQL skills; familiarity with forecasting libraries such as Prophet or Nixtla is beneficial.
• Experience designing scalable data pipelines and production-ready analytical tools beyond simple spreadsheets.
• Deep understanding of SaaS financial metrics including Net Revenue Retention, LTV, churn, and how product usage drives revenue.
• Strong ability to translate technical work into insightful business narratives that influence decision-making.
• Excellent business judgment with a product-focused perspective on customer behaviors underlying data.
• Familiarity with AI-powered development tools to expedite data pipeline creation, model prototyping, and documentation.

Success Indicators

• Deployment of automated forecasting models that enhance accuracy and granularity while lowering manual effort.
• Inclusion of a reliable Propensity Score predictive of customer expansion and contraction within financial planning processes.
• Implementation of scalable coded workflows accelerating time-to-insight for the Existing Business team.
• High leadership confidence in forecasting the financial impact of evolving customer usage patterns.

Employee Benefits

• Competitive salary and equity participation within a rapidly expanding startup environment.
• Complimentary weekday lunches and a stocked kitchen with snacks.
• Regular compensation reviews rewarding excellent performance.
• Pension plan with up to 4% match.
• Comprehensive life insurance plus health and dental coverage for employees and dependents.
• Flexible paid time off policies.
• Paid maternity leave and 6 weeks paternity leave for new parents.
• Cycle-to-Work Scheme including secure bike storage.
• Provision of MacBooks as standard equipment, with Windows offered for specific roles as required.

Company Policies and Culture

Fin operates a hybrid working model requiring employees to be onsite at least three days per week to foster collaboration and culture. The company embraces an open and inclusive environment, focusing exclusively on achieving organizational goals while respecting diverse personal opinions on external social or political issues. Fin is committed to Equal Employment Opportunity and does not discriminate on any legally protected basis.