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Senior Data Scientist - Payment Processing
12 month Fixed Term Contract
Onsite / Hybrid
Location: Amsterdam
Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner
Role Details:
We are seeking a highly experienced Senior Data Scientist to drive advanced analytics, machine learning, and predictive modelling initiatives for a leading global payment processing organization. The role focuses on building scalable AI/ML solutions that enhance fraud detection, credit risk assessment, merchant analytics, customer insights, operational efficiency, and decision intelligence across global payment ecosystems.
The role requires experience working with high‑volume transactional data, real‑time analytics, and compliance-driven financial environments.
Domain Experience:
Experience in payment processing, fintech, banking, or financial services preferred.
Understanding of operational workflows like incident management, reconciliation, risk assessment, dispute handling, or merchant onboarding.
Roles and Responsibilities:
Design and develop end-to-end ML models for fraud detection, anomaly detection, risk scoring, credit decisioning, transaction classification, and chargeback prediction.
Build predictive models using supervised, unsupervised, and deep learning techniques.
Lead research, experimentation, and model benchmarking to identify optimal algorithms.
Implement real-time scoring pipelines in low-latency payment environments.
Analyse large-scale payment transaction datasets to identify trends, patterns, and actionable insights.
Perform feature engineering for structured, semi-structured, and time-series data.
Work with data engineers to optimize data pipelines feeding ML/AI workloads.
Collaborate on data quality checks, lineage tracking, and metadata governance.
Partner with Fraud Operations, Risk, and Compliance teams to refine models and rules.
Develop explainable AI approaches for regulatory and audit requirements.
Mentor junior data scientists and analysts on ML methodologies and best practices.
Lead cross-functional initiatives with Data Engineering, Platform Engineering, BI, and Product.
Technical Skills:
Python (NumPy, pandas, scikit-learn, PySpark, TensorFlow/PyTorch)
SQL & distributed computing frameworks
Clustering, Supervised & Unsupervised learning, GenAI project experience
Time-series modelling & anomaly detection
NLP, deep learning, statistical modelling
Strong understanding of: Real-time ML systems, High-volume transactional data, Cloud ML platforms (Azure/AWS/GCP), Feature stores and ML pipelines
Non-Technical Skills:
Work with cross-functional teams: Business SMEs, Data Scientists, Developers, Platform Admins, and Product Owners.
Lead requirement discovery sessions to map business needs into scalable data-driven app and automation designs.
Mentor junior developers and provide technical leadership across projects.
Strong stakeholder management and communication capabilities.
Ability to translate business requirements into scalable data architectures.
Experience working in global, multi‑regional delivery models.
Strong analytical and problem‑solving mindset with ownership.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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