Senior Finance Data Scientist, Existing Business

Intercom Intercom · Enterprise · Dublin, Ireland · Finance & Business Operations

Senior Finance Data Scientist role focused on building production-grade forecasting and predictive models for SaaS revenue, customer LTV, and expansion/churn propensity. This role involves data pipeline engineering, SQL, Python, and translating complex financial and behavioral data into actionable insights for leadership. While the company is AI-focused (AI Customer Service), this specific role is finance-data-science focused, using AI tools for productivity but not building core AI/ML models as the primary deliverable.

What you'd actually do

  1. Build and maintain predictive models for usage-based revenue, renewals, and expansion that outperform traditional linear forecasts.
  2. Develop propensity models to identify expansion opportunities and churn risks before they materialize in the ledger.
  3. Design and maintain curated datasets that serve as the single source of truth.
  4. Define and iterate on our LTV frameworks, providing a clear linkage between product engagement and long-term financial outcomes.
  5. Build automated, code-based forecasting workflows that increase the speed, reliability, and granularity of our financial planning.

Skills

Required

  • 3+ years in Data Science, Strategic Finance, or Revenue Analytics
  • Deep focus on SaaS or usage-based business models
  • High proficiency in Python (pandas, scikit-learn)
  • 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
  • Business Judgment
  • AI-Augmented Productivity (e.g. Cursor, Claude Code)

Nice to have

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

What the JD emphasized

  • production-grade forecasting models
  • predictive models
  • customer LTV
  • expansion propensity
  • churn risks
  • curated datasets
  • automated, code-based forecasting workflows
  • probabilistic and time-series models
  • expansion propensity and LTV frameworks
  • backtesting and iteration
  • curated, model-ready datasets
  • production-quality SQL and Python
  • scenario modeling and sensitivity analysis
  • clear, decision-oriented narratives
  • Advanced Technical Skills
  • Expert-level SQL
  • System Design Mindset
  • SaaS Mastery
  • Automated forecasting models