Requirements:

You have 5+ years of experience as a machine learning or data scientist;
You have experience with the full machine learning model lifecycle in production flows;
You have experience leveraging a big data framework to create the pipelines needed to feed the models with appropriate data;
You have a good understanding of software engineering practices as well as data engineering and MLOps principles;
You have knowledge of data science and statistics and machine learning techniques. These include a strong grounding in statistical inference, machine learning for prediction, and causal inference - Deep Learning experience is a plus;
You have 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 Airflow;
You have an experimental mindset with a launch fast and iterate mentality. Extensive experience with designing and running experiments is essential;
You are proactively taking the lead in projects, from ideation to deployment. You have experience working with a wide range of stakeholders and can clearly communicate complex outcomes over a wide range of audiences;
You stay up-to-date with the latest research and developments in the machine learning field, and apply this where valuable;

What You'll Be Doing:

Research, design, implement, train, deploy and monitor machine learning algorithms, either for batch prediction or real-time. Examples include: on-line 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 our experiments, training runs, validation runs and monitoring before, during and after deployment. Furthermore, collaborate with MLOps to improve our 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;