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# Senior Finance Data Scientist

- **Company:** Capital on Tap
- **Location:** London, UK
- **Experience:** Expert
- **Salary:** £88,843.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Microsoft Azure, Monitoring of Systems, Python (Programming Language), SQL Databases, Snowflake, Kubernetes, Xgboost
- **Published:** August 14, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5841931579

## About the Role

* 2-5+ years building statistical/credit risk models in Python, and extracting and shaping data in SQL.
 * Credit risk modelling experience, ideally IFRS9 ECL (PD/LGD/EAD), staging and model monitoring - or a clear desire to build that expertise quickly.
 * Commercial understanding of revolving credit economics (utilisation, arrears, default, recoveries, portfolio growth).
 * Comfort working end-to-end - from analysis and calibration through to production runs and stakeholder-ready forecast inputs.
 * Curiosity and judgement in applying AI to modelling and engineering work, while keeping outputs explainable and controlled.
 * Ability to work autonomously with strong judgement and proactivity in a small team.

Nice to Have

 * Experience with some of our stack is helpful - Snowflake, dbt, Fivetran, Python, JupyterLab, Omni, Kubernetes on Azure.
 * Experience feeding portfolio/behavioural models into an FP&A or financial planning model.
 * Familiarity with LightGBM or similar gradient-boosted models for behavioural forecasts.
 * Exposure to model change control, independent validation, or year-end ECL disclosure packs.
 * Prior work across both UK and US (or multi-region) credit portfolios.

## Description

* Build, calibrate, implement and monitor IFRS9 models (PD, LGD, EAD), including staging/SICR logic and macroeconomic overlays.
 * Run and harden month-end ECL production - scenario runs, combined outputs, and result checks into Snowflake.
 * Support provisioning, impact analysis and year-end submissions with clear audit trails and documentation.

Portfolio Modelling

 * Develop and maintain the portfolio model that feeds the financial model - active customers, balances, funding, repayments, finance charges and bad debt.
 * Build and backtest forecasting components (spend, cashflow, cohort/segment rate curves) in Python and dbt.
 * Diagnose drift, recalibrate assumptions and overlays, and deliver forecast inputs Finance can trust.

Across Both

 * Partner with FP&A, Risk and other stakeholders so model outputs land cleanly in the financial forecast, without unnecessary process or IT complexity.
 * Improve monitoring, automation and documentation so monthly cycles are repeatable and explainable.
 * Use AI tools confidently across the workflow, and help the team adopt practical AI-assisted ways of working.
 * Coach colleagues on modelling methods and our data tools where needed.


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