> Markdown version of [/jobs/ext/2258538-fraud-data-scientist](https://www.wearedevelopers.com/jobs/ext/2258538-fraud-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Fraud Data Scientist - **Company:** Checkout.com - **Location:** Greater London, UK - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** A/B Testing, Airflow, Big Data, BigQuery, Cloud Database, Data Warehousing, Python (Programming Language), Machine Learning, NumPy, Standard Sql, Feature Engineering, Large Language Models, Pandas, Scikit Learn, Xgboost, Machine Learning Operations, Databricks - **Published:** August 26, 2026 - **Apply:** https://www.collegerecruiter.com/job/2815095955-fraud-data-scientist ## About the Role * Experience: Minimum of 3 years of applied Data Science experience with a proven track record across fintech domains, with experience in fraud, risk, or payments preferred. * Production Expertise: Proven, hands-on experience deploying and maintaining machine learning models in high-traffic production environments is required, with real-time experience preferred. * Data Science Tech Stack: Expert-level Python programming (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM) and exceptional SQL skills for querying massive, complex datasets. * Data Environment: Robust experience working within cloud data environments like Databricks, and querying/manipulating large-scale datasets in data warehouses like BigQuery. * Orchestration & MLOps: Practical experience with machine learning lifecycle and orchestration tools, such as MLflow and Airflow. * Business-Impact Focus: A strong ability to translate raw model results into real-world business outcomes. You know how to balance technical model performance (precision/recall) with financial impact, operational realities, and the user experience. ## Description About the Role As a Fraud Data Scientist, you will be at the front lines of protecting our ecosystem from sophisticated financial fraud and abuse. You will join a high-impact team operating in a data-rich, high-frequency environment where seconds matter. In this role, you will take ownership of the end-to-end machine learning lifecycle-from uncovering complex fraud patterns to deploying highly scalable, real-time models into production. You will collaborate closely with Engineering, Product, and Risk Operations to build robust defenses that balance strict security with a seamless user experience. What You Will Be Doing * Model Development & Deployment: Design, train, and deploy advanced machine learning models (e.g., gradient boosting, anomaly detection, graph networks) to detect and mitigate fraud in real-time. * Production Ownership: Take full ownership of putting models into production systems, ensuring low-latency execution and high reliability. * Agentic Workflows: Research, build, and implement Agentic flows and LLM-driven orchestration to automate multi-step fraud decisioning, logic routing, and investigation paths. * Adversarial Analysis: Conduct deep-dive exploratory analysis on massive datasets to identify emerging fraud vectors, loops, and coordinated attacks. * Feature Engineering: Build and optimize real-time streaming and batch features to improve model signal and precision. * Experimentation & Monitoring: Design rigorous shadow-testing and A/B testing frameworks for new models. Set up continuous monitoring pipelines to catch data drift and performance degradation early. Requirements * Experience: Minimum of 3 years of applied Data Science experience with a proven track record across fintech domains, with experience in fraud, risk, or payments preferred. * Production Expertise: Proven, hands-on experience deploying and maintaining machine learning models in high-traffic production environments is required, with real-time experience preferred. * Data Science Tech Stack: Expert-level Python programming (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM) and exceptional SQL skills for querying massive, complex datasets. * Data Environment: Robust experience working within cloud data environments like Databricks, and querying/manipulating large-scale datasets in data warehouses like BigQuery. * Orchestration & MLOps: Practical experience with machine learning lifecycle and orchestration tools, such as MLflow and Airflow. * Business-Impact Focus: A strong ability to translate raw model results into real-world business outcomes. 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