Machine Learning Scientist

Salary Range: 50000 to 70000 (Currency: EUR) (Pay period: per-year-salary)

What You Will Do

• Design, develop, and evaluate machine learning models for fraud detection, including fraud scoring and ensemble models that combine multiple sources of evidence.

• Analyze large-scale datasets to identify complex patterns, relationships, behaviors, and anomalies, developing new signals to improve fraud detection.

• Define robust experimentation and evaluation methodologies, including model calibration, operating thresholds, and performance metrics under realistic production conditions.

• Develop interpretable and explainable ML approaches that help internal stakeholders and customers understand fraud scores and decisions.

• Explore innovative approaches to fraud decisioning, including human-in-the-loop techniques and causal methods.

• Collaborate with Product and Fraud Domain Experts to translate real-world fraud challenges and production feedback into effective machine learning capabilities.

• Partner with Data Engineering to transform ML prototypes into scalable, maintainable, production-ready solutions.

• Contribute to model monitoring, governance, technical documentation, ML tooling, and shared best practices across the team.

What You Need

• A Bachelor's, Master's, or PhD degree in Computer Science, Statistics, Mathematics, Machine Learning, or a related quantitative field, or equivalent practical experience.

• Skills and expertise typically gained through approximately 5 years of experience as a Data Scientist, Applied ML Scientist, or in a comparable role.

• Strong knowledge of statistical methods, supervised machine learning, and model evaluation, particularly for highly imbalanced classification problems.

• Hands-on experience in feature engineering and machine learning model development using complex behavioral, relational, or contextual data.

• Strong programming and analytical skills using Python, PySpark, and SQL.

• Experience working with cloud-based machine learning and data infrastructure.

What Would Be Nice

• Experience in fraud detection, financial crime, payments, digital identity, risk, or Trust & Safety.
• Knowledge of advanced machine learning approaches such as graph-based methods, representation learning, or causal AI.
• Experience with modern data lakehouse or real-time data technologies such as Apache Iceberg or Apache Pinot.
• Experience taking machine learning solutions from experimentation into scalable production environments.
• Confidence working in Linux environment.

Who You Are

• A pragmatic and hands-on machine learning practitioner who focuses on solving meaningful business problems rather than adding unnecessary model complexity.

• Naturally curious, with a passion for discovering patterns and relationships in data and experimenting with new approaches.

• Comfortable using data and evidence to challenge assumptions and influence technical and product decisions.

• Collaborative and willing to share knowledge, mentor colleagues, and contribute to continuous improvement.

• A confident communicator with strong spoken and written English skills.

• Comfortable collaborating with distributed teams across different offices and time zones.

• Self-motivated, adaptable, and able to work autonomously while contributing positively to a team.

What Success Looks Like

• Deliver measurable impact: Develop and improve machine learning models that strengthen fraud detection, support more accurate decisions, and deliver tangible business value.

• Turn insights into innovation: Identify meaningful patterns, behaviors, and relationships in complex datasets and translate them into effective fraud detection signals.

• Build reliable, explainable ML solutions: Create models that are robust, interpretable, and evaluated against realistic production conditions.

• Take ideas into production: Work closely with Data Engineering to transform promising ML experiments into scalable, maintainable solutions.

• Drive continuous improvement: Use experimentation, production feedback, and emerging ML techniques to improve model performance and fraud decisioning.

• Make an impact through collaboration: Partner with Product, Fraud Domain Experts, and technical teams to solve complex challenges, share knowledge, and advance ML best practices.