Data Scientist

We are looking for a Data Scientist to build and deploy end-to-end machine learning solutions that power key fintech use cases such as credit risk modeling, customer lifecycle optimization, fraud detection, and collections strategies.

This is a role where your work directly influences key business decisions across risk, growth, and collections. As a Data Scientist, you’ll operate at the intersection of data, product, and engineering—developing scalable, production-level machine learning solutions in a modern Azure ML environment. You will take ownership of the full ML lifecycle, from problem definition to deployment and monitoring, while collaborating closely with stakeholders to turn data into insights that drive real, measurable impact.

• Own the full lifecycle of machine learning solutions: problem framing, data exploration, feature engineering, model development, deployment, and monitoring
• Develop and productionize models for credit scoring, marketing optimization, collections, and other core business problems
• Work with large-scale data to build robust data pipelines and feature datasets
• Deploy and manage models using Azure Machine Learning, including experiment tracking, model versioning, and lifecycle management
• Collaborate closely with stakeholders (product, risk, marketing, engineering) to identify high-impact opportunities and translate them into data solutions
• Design, implement, and analyze A/B tests and experiments, ensuring statistically sound and business-relevant conclusions
• Build monitoring frameworks to track model performance, detect data/model drift, and ensure long-term reliability
• Ensure models meet regulatory and explainability requirements (e.g., credit decision transparency)
• Communicate insights and model behavior clearly to both technical and non-technical stakeholders

The salary range is €4,000 to €5,500 gross monthly. Our final offer to you will be set up fairly, considering your skills and experience.

We may use artificial intelligence (AI) tools to support specific parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses against predefined criteria. These tools assist our recruitment team but do not replace human judgment. All final hiring decisions are made by human recruiters.

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