Machine Learning Scientist I - Protect

This is Adyen

Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition.

For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster.

Machine Learning Scientist I - Protect

Adyen is looking for a Machine Learning Scientist I to join our Protect team in Amsterdam. As part of Protect, you will help us build the next generation of real-time fraud prevention systems.

Our team owns the machine learning models embedded directly into Adyen’s core payment flow, scoring over 100 million transactions daily to stop evolving fraud in real time. In this role, you will work alongside experienced Machine Learning Scientists, Engineers, and Data Analysts to build, train, deploy, and monitor scalable models that secure global online payments.

What You Will Do:

• Develop & Train ML Models: Assist in researching, training, and evaluating machine learning models used for real-time risk assessment, anomaly detection, behavioural prediction, and fraud prevention.
• Pipeline Development: Help build and maintain batch and streaming data pipelines to feed online feature stores and analytical workflows.
• Model Lifecycle & Monitoring: Support the continuous improvement of our models by contributing to ongoing automated experiments, validation runs, and real-time performance monitoring.
• Cross-Functional Collaboration: Partner closely with peers in engineering, product, and analytics to translate business requirements into actionable machine learning tasks and merchant-facing risk tools.
• Data Exploration: Analyze large, complex payment datasets to uncover fraud trends, identify patterns, and spot opportunities for ML-driven improvements.
• Experimentation: Participate in model evaluation and experimentation (e.g., A/B testing) to measure model accuracy, efficiency, and real-world business impact.

Who You Are:

• Background: You hold a Bachelor’s or Master’s in Computer Science, Data Science, Mathematics, Statistics, or a related quantitative field (or equivalent hands-on experience).
• Core ML Knowledge: Solid grounding in statistical inference, machine learning fundamentals, and predictive modeling (e.g., supervised learning, deep learning, tree-based models like XGBoost/LightGBM).
• Toolkit & Languages: Proficiency in Python and SQL. Familiarity with standard data science libraries (e.g., Pandas, PyTorch, TensorFlow, Scikit-Learn) and distributed computing frameworks (e.g., PySpark) is highly desirable.
• Engineering & MLOps Foundations: Strong interest in clean code, software engineering best practices, and standard MLOps tooling (e.g., MLFlow, Airflow, Git).
• Mindset: You possess a strong curiosity, an experimental mindset, and a passion for learning. You thrive in a collaborative environment where you can launch fast and iterate based on real feedback.

Our Diversity, Equity and Inclusion commitments

Our unique approach is a product of our diverse perspectives. This diversity of backgrounds and cultures is essential in helping us maintain our momentum. Our business and technical challenges are unique, and we need as many different voices as possible to join us in solving them - voices like yours. No matter who you are or where you’re from, we welcome you to be your true self at Adyen.

Studies show that women and members of underrepresented communities apply for jobs only if they meet 100% of the qualifications. Does this sound like you? If so, Adyen encourages you to reconsider and apply. We look forward to your application!

What’s next?

Ensuring a smooth and enjoyable candidate experience is critical for us. We aim to get back to you regarding your application within 5 business days. Our interview process tends to take about 4 weeks to complete, but may fluctuate depending on the role. Learn more about our hiring process here. Don’t be afraid to let us know if you need more flexibility.

This role is based out of our Amsterdam office. We are an office-first company and value in-person collaboration; we do not offer remote-only roles.