Responsibilities

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.

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.

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.

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.

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.

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.