# Machine Learning Scientist
Adyen is looking for a Senior Machine Learning Scientist to join the team in Amsterdam. You will sit at the cornerstone of algorithms, mathematics, and engineering, solving problems by designing and implementing production-ready machine learning solutions. You will be responsible for building, developing and deploying algorithms that power data products at Adyen.
## In this role, you will:
- Research, design, implement, train, deploy and monitor machine learning algorithms, either for batch prediction or real-time. Examples include: online learning algorithms to pick the best optimization decision in a changing environment, clustering algorithms to group customers/shoppers, supervised and semi-supervised learning methods for inference on risk patterns or graph analysis, representation learning for behavior prediction and monitoring, Anti-Money Laundering (AML) systems and real-time anomaly detection based on time-series modeling
- Develop orchestrated pipelines for analytical purposes and machine learning training
- Contribute to ongoing automation efforts for experiments, training runs, validation runs and monitoring before, during and after deployment. Collaborate with MLOps to improve machine learning tooling
- Collaborate closely with product managers and business stakeholders to understand requirements, define problems, and frame them as solvable machine learning tasks
- Explore and analyze large, complex datasets to identify patterns, insights, and opportunities for ML-driven solutions
- Define key performance metrics, design rigorous experiments (e.g., A/B tests), and perform statistical analysis to validate model performance and quantify business impact
## Who you are:
- 5+ years of experience as a machine learning engineer or data scientist
- Experience with the full machine learning model lifecycle in production flows
- Experience leveraging a big data framework to create the pipelines needed to feed the models with appropriate data
- Good understanding of software engineering practices as well as data engineering and MLOps principles
- Knowledge of data science and statistics and machine learning techniques. Strong grounding in statistical inference, machine learning for prediction, and causal inference - Deep Learning experience is a plus
- Strong familiarity with the standard data science toolkit, such as (py)spark, (Trino) SQL, Tensorflow, PyTorch, XGBoost/LightGBM, Pandas, MLFlow or similar MLOps frameworks, and Air