Data Scientist

Data Scientist | Sure Pay | Freelance/Contract

Location: Hybrid from Utrecht | Fin Tech SaaS | Fraud & AML Discovery Team

Help us stop fraud before it starts. At Sure Pay, we’ve processed over 10 billion IBAN‑Name Checks keeping payments secure and fraud‑free for over 200 banks and 300+ corporates across Europe. We’re building a new generation of fraud and anti‑money laundering (AML) products and need a hands‑on Data Scientist to join our Discovery team.

• Explore large financial datasets to uncover patterns, edge cases, and insights
• Build quick‑turnaround ML prototypes to validate hypotheses and identify signal quality
• Select the right algorithms based on the problem (supervised, unsupervised, anomaly detection, etc.)
• Fine‑tune models with awareness of overfitting, false positives, and interpretability
• Work closely with product and ML engineering to turn POCs into scalable systems
• Help define how we measure success for fraud detection (e.g., recall vs. precision, AUC, business cost)
• Champion model explainability and ethical ML practices
• Contribute ideas, structure, and rigor to an early‑stage product team

• 3–5 years of experience in Data Science roles, ideally within fraud, security, or high‑noise domains
• Strong statistical and analytical skills. You should be able to reason clearly about data and model performance
• Proficient in Python and key ML libraries (e.g., pandas, scikit‑learn, PyTorch, MLflow)
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Experience designing, tuning, and evaluating machine learning models
• Knowledge of data pipelines, basic backend development, and infrastructure awareness
• Comfortable with ambiguity, iteration, and early‑stage product work
• Familiarity with model interpretability tools and techniques

• Experience in AML, fraud prevention, or transaction monitoring
• Exposure to systems like FCRM, NICE Actimize, Risk Shield, or Pega
• CI/CD practices applied to ML systems
• Interest in generative AI and prompt engineering

Design the foundations of a new AML product with real‑world stakes. Work closely with smart engineers, learn from real customer data, and push the boundaries of what’s possible in fraud detection.

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