At a Glance
- Tasks: Protect our ecosystem from fraud using advanced machine learning techniques.
- Company: Join a leading fintech company dedicated to security and innovation.
- Benefits: Competitive salary, flexible hours, and opportunities for professional growth.
- Other info: Collaborative team culture with a focus on cutting-edge technology.
- Why this job: Make a real impact by combating financial fraud in a fast-paced environment.
- Qualifications: 3+ years in data science with expertise in machine learning and fraud detection.
The predicted salary is between 70000 - 90000 £ per year.
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. You know how to balance technical model performance (precision/recall) with financial impact, operational realities, and the user experience.
Fraud Data Scientist employer: Checkout.com
Checkout.com is an exceptional employer that champions a flexible hybrid working model, allowing employees to balance their professional and personal lives effectively. With a strong emphasis on growth and collaboration, the company provides ample opportunities for career development while working alongside talented teams in the dynamic financial services sector in London. Joining Checkout.com means being part of a forward-thinking organisation that values compliance and innovation in payments and product regulation.
