Asks for

The ad says“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.”
The ad says“Orchestration & MLOps: Practical experience with machine learning lifecycle and orchestration tools, such as MLflow and Airflow.”
The ad says“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.”
The ad says“Data Environment: Robust experience working within cloud data environments like Databricks, and querying/manipulating large-scale datasets in data warehouses like BigQuery.”
The ad says“Data Environment: Robust experience working within cloud data environments like Databricks, and querying/manipulating large-scale datasets in data warehouses like BigQuery.”
The ad says“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.”
The ad says“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.”
The ad says“Data Science Tech Stack: Expert-level Python programming (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM) and exceptional SQL skills for querying massive, complex datasets.”
The ad says“Agentic Workflows: Research, build, and implement Agentic flows and LLM-driven orchestration to automate multi-step fraud decisioning, logic routing, and investigation paths.”
The ad says“Orchestration & MLOps: Practical experience with machine learning lifecycle and orchestration tools, such as MLflow and Airflow.”
The ad says“Orchestration & MLOps: Practical experience with machine learning lifecycle and orchestration tools, such as MLflow and Airflow.”
The ad says“Data Science Tech Stack: Expert-level Python programming (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM) and exceptional SQL skills for querying massive, complex datasets.”
The ad says“Data Science Tech Stack: Expert-level Python programming (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM) and exceptional SQL skills for querying massive, complex datasets.”
The ad says“Data Science Tech Stack: Expert-level Python programming (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM) and exceptional SQL skills for querying massive, complex datasets.”
The ad says“Data Science Tech Stack: Expert-level Python programming (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM) and exceptional SQL skills for querying massive, complex datasets.”
The ad says“Data Science Tech Stack: Expert-level Python programming (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM) and exceptional SQL skills for querying massive, complex datasets.”
The ad says“Data Science Tech Stack: Expert-level Python programming (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM) and exceptional SQL skills for querying massive, complex datasets.”

Company Description

We’re Checkout.com. You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day.

We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers.

Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale.

If you want to do career-defining work, you’ve come to the right place. We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact.

With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started.

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.

Additional Information

Bring all of you to work

We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one.

Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity, and where your growth is in your hands.

We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here.

It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable.

Life at Checkout.com

We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.

Curious about what it’s like to be part of our team? Visit our Careers Page to learn more about our culture, open roles, and what drives us.

For a closer look at daily life at Checkout.com, follow us on LinkedIn and Instagram