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# Data Scientist

- **Company:** Checkout.com
- **Location:** London, UK
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Encodings, Information Leak Prevention, Data Transformation, Python (Programming Language), Unix Shell, Machine Learning, Performance Tuning, Recommender Systems, Feature Engineering, Large Language Models, Model Validation, AWS Lambda, Docker
- **Published:** July 15, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=9929be96d9e93fa7

## About the Role

* 3+ years of experience developing machine learning models to solve business problems.
 * Strong understanding of supervised ML algorithms, tuning, and performance evaluation.
 * Experience with a range of feature engineering techniques (e.g. target encoding).
 * Solid grasp of frequentist and Bayesian statistics for parameter estimation and experimentation.
 * Experience in writing clean, production-grade Python code for both model training and inference.
 * Excited to leverage LLMs for coding support and process optimisation to maximise personal and team productivity., * Experience with advanced data transformation techniques (e.g., lambda functions).
 * Familiarity with, or hands-on experience in, recommender systems, contextual bandits, or network intelligence applications.
 * Experience in fintech, payments, or building cross-disciplinary relationships for advice and guidance.
 * Familiarity with the unix shell, Databrics, Docker, and common cloud platforms (GCP/AWS).

## Description

Checkout.com is looking for a Data Scientist to join our ambitious team, focused on discovering, designing, and experimenting with new estimators, models and features to boost payment performance across our portfolio of merchants. You will work closely with Data Scientists, Product and Engineering to enhance our core offering, protect customer lifetime value through network intelligence, and ensure safe model launches through robust observability., * Contribute to the research and development of new ML models and estimators to boost core Acceptance Rate performance.

 * Design and implement experiments to produce actionable insights, focusing on managing time-based data leakage and ensuring robust model evaluation.
 * Collaborate with other Data Scientists and engineers to productionise ML features, models and evolve our evaluation and monitoring frameworks.
 * Write high-quality, interpretable Python code for feature engineering and model training, contributing directly to our core products.
 * Communicate hypotheses, evaluation results, and monitoring dashboards clearly to both technical and non-technical audiences.


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