About SurePay

Founded in 2016, SurePay is the leading account verification platform, relied upon by 250+ banks and thousands of organizations, to prevent fraud, misdirected payments, and reputational damage. Headquartered in Utrecht, we are scaling our platform and our team across Europe and beyond.

About the role

As part of SurePay’s strategic growth, we are expanding beyond our core Account Verification services to launch an integrated suite of actionable Fraud and AML risk intelligence products. To drive this, we have established a dedicated domain: Fraud Risk Intelligence. You will join a focused cross-functional team and your primary anchor project is designing and launching our centralized transaction risk-scoring engine. Within the team, you will collaborate closely with our other Data Scientist, focussing on translating complex statistical concepts into modular, production-ready prototypes. Responsibilities include:

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

About you

This is an early stage product team so we are looking for someone eager to contribute with ideas, structure, and rigor and who enjoys solving messy problems and working across the full stack of data science. You bring structure to chaos, prototype quickly, and care about solving real-world problems, not just building cool models. Furthermore:

• 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)
• Experience designing, tuning, and evaluating machine learning models
• Knowledge of data pipelines, basic backend development, and infrastructure awareness
• Familiarity with model interpretability tools and techniques

What we offer

• Competitive salary + 8% personal benefits budget (use it on training, more time off, or your salary)
• 25 holidays 
• Hybrid working setup 
• MacBook Pro, iPhone, and all the tech you need
• Travel costs covered (NS Business Card)
• Pension plan
• A culture of ownership, innovation, and autonomy
• Friday drinks, offsites, and a quarterly company meetup
• A diverse team of 35+ nationalities, driven by impact