# Senior Finance Data Scientist

**Company:** [Intercom](https://hotfix.jobs/companies/intercom)
**Location:** Dublin, Ireland
**Role:** Data Science
**Experience:** 5+ years
**Skills:** Python, pandas, scikit-learn, SQL, Prophet, Nixtla, Data Pipelines, Time-Series Modeling, Probabilistic Modeling, Forecasting, Saas Analytics, Product Analytics, Cursor, Claude Code
**Posted:** 2026-07-20

> Build production-grade forecasting, propensity, and LTV models that connect customer behavior and product usage to SaaS revenue outcomes. The role requires advanced Python and SQL, scalable data-pipeline experience, and the ability to communicate insights to finance and executive stakeholders.

## Job Description

## Responsibilities

### 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 signals.
- Evaluate model performance through backtesting and iteration.

### Data and Analytical Infrastructure
- Own the end-to-end finance data pipeline, transforming raw product usage and billing data into curated, model-ready datasets in the data warehouse.
- Write and optimize production-quality SQL and Python for large-scale datasets and automated FP&A workflows.
- Ensure data integrity and consistency across predictive systems and executive dashboards.
- Contribute to the long-term data strategy for tracking and predicting Existing Business health.

### Analytical Problem Solving
- Translate ambiguous business questions into structured data science projects.
- Connect ARR outcomes to product adoption, customer health scores, and go-to-market activity.
- Perform scenario modeling and sensitivity analysis to explain possible NRR outcomes.

### Business Partnership and Communication
- Partner with Sales, Product, and Data Engineering to align financial models with customer behavior and product roadmaps.
- Translate complex statistical outputs into clear, decision-oriented narratives for the CFO and executive leadership.
- Build executive-ready predictive dashboards and strategic presentations.

## Requirements
- 3+ years of experience in Data Science, Strategic Finance, or Revenue Analytics, focused on SaaS or usage-based business models.
- High proficiency in Python, including pandas and scikit-learn.
- Expert-level SQL skills.
- 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.
- Strong business judgment and product sense.
- Proficiency with AI-native development tools such as Cursor and Claude Code.

## Nice to Have
- Experience with forecasting libraries such as Prophet or Nixtla.

## Success Measures
- Automated forecasting models that are more accurate, granular, and less manual than previous iterations.
- A propensity score integrated into planning that predicts customer expansion and contraction.
- Scalable, code-based workflows that reduce time to insight.
- Strong leadership confidence in forecasts of the financial impact of changing customer usage patterns.

## Compensation and Benefits
- Competitive salary and equity.
- Lunch provided every weekday, snacks, and a fully stocked kitchen.
- Regular compensation reviews.
- Pension scheme with employer matching up to 4%.
- Life assurance and comprehensive health and dental insurance for employees and dependents.
- Flexible paid time off.
- Paid maternity leave and six weeks of paternity leave.
- Cycle-to-Work Scheme and secure bike storage.
- MacBook standard, with Windows available for certain roles.
- Hybrid working policy requiring at least three days per week in the office.

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