A leading banking and payments organisation in Dubai is actively seeking a Senior Fraud Data Scientist to design, develop, and deploy cutting-edge card fraud detection models for issuing and acquiring portfolios. This is a full-time, senior-level role requiring 7+ years of data science experience — with at least 3 years specifically in card fraud modelling — and strong expertise in SAS SFD, Python, MLOps, and advanced machine learning frameworks. If you are a fraud-specialist data scientist who wants your models to protect real financial systems at scale, this is your next career move in Dubai 2026.
About the Role — Financial Crime Data Science
Domain: Card Fraud Detection — Issuing & Acquiring Portfolios
Sector: Banking, Financial Services & Payments Industry
Model Hosting: SAS SFD — production-grade deployment and scalability
Regulation: Compliance with fraud risk management regulatory requirements
Impact: Direct reduction of fraud losses across live transaction portfolios
Core Technology Stack
Languages: SAS (incl. SAS SFD), Python, SQL
ML Frameworks: TensorFlow, PyTorch, Scikit-learn
MLOps Tools: MLflow, Kubeflow, CI/CD pipelines
Cloud Platforms: AWS, Microsoft Azure, Google Cloud Platform (GCP)
Specialisation: Card fraud detection, transaction data, real-time inference
Position Overview
The Senior Fraud Data Scientist role in Dubai sits at the frontier of financial crime prevention and machine learning engineering. You will own the full lifecycle of card fraud detection models — from initial design and statistical development through to production deployment, MLOps automation, continuous monitoring, and iterative optimisation. Your models will operate in live banking environments, processing real transaction data to protect customers and minimise financial loss in real time.
This is not an advisory or research-only position. You will be hands-on across model development, deployment, validation, and stakeholder communication — bridging the gap between advanced data science and production-grade financial systems in one of the most technically demanding domains in the global banking industry.
Why This Role Matters: Card fraud costs the global financial system billions of dollars every year — and Dubai’s position as a major payments hub makes this work especially critical. As a Senior Fraud Data Scientist, your models will directly determine how much fraud is caught, how many genuine customers are incorrectly blocked, and how efficiently the fraud operations team can respond. This is high-stakes, high-impact data science where your technical expertise has real, measurable consequences every single day.Why This Role Matters: Card fraud costs the global financial system billions of dollars every year — and Dubai’s position as a major payments hub makes this work especially critical. As a Senior Fraud Data Scientist, your models will directly determine how much fraud is caught, how many genuine customers are incorrectly blocked, and how efficiently the fraud operations team can respond. This is high-stakes, high-impact data science where your technical expertise has real, measurable consequences every single day.
Key Responsibilities
Card Fraud Model Development & Optimisation
- Design, develop, and continuously optimise card fraud detection models for both issuing and acquiring portfolios across live transaction environments
- Implement advanced statistical techniques, machine learning algorithms, and AI-driven approaches to dramatically improve fraud detection accuracy and precision while minimising disruptive false positives for genuine customers
- Develop and refine feature engineering pipelines that extract maximum signal from complex, high-volume transaction data streams in real time and batch processing contexts
- Benchmark model performance against industry standards and continuously identify opportunities to push fraud detection capability beyond existing baselines
Model Hosting & Production Deployment
- Deploy fraud detection models using SAS SFD and seamlessly integrate them with production banking systems and transaction processing infrastructure
- Ensure smooth, scalable model hosting across multiple environments — development, staging, and production — with full version control and rollback capability
- Design deployment architectures that support low-latency, high-throughput real-time scoring for card fraud decisions at the point of transaction
- Work with cloud platforms including AWS, Azure, or GCP to enable cloud-hosted model inference and scalable data processing where required
MLOps & Automation Pipelines
- Establish and maintain robust MLOps pipelines using tools such as MLflow and Kubeflow for continuous integration, deployment, and monitoring of all fraud detection models in production
- Automate model retraining workflows to ensure fraud models adapt rapidly to shifting fraud patterns and emerging attack vectors without requiring manual intervention
- Build performance tracking systems and automated alerting to flag model degradation, data drift, or anomalous fraud pattern shifts before they impact business outcomes
- Implement CI/CD pipeline best practices for model code, ensuring reliable, auditable, and repeatable model release processes across all environments
Model Evaluation, Testing & Validation
- Conduct rigorous model validation, stress testing, and performance benchmarking to ensure fraud models perform reliably under production load and edge case conditions
- Collaborate closely with fraud operations teams to validate that model outputs align with real-world fraud investigation outcomes and operational workflow requirements
- Ensure all fraud models meet applicable business requirements, internal risk standards, and regulatory compliance obligations within the UAE banking and payments regulatory framework
- Document model methodology, validation outcomes, and governance records in a structured manner suitable for internal audit, regulatory review, and senior management reporting
Collaboration & Stakeholder Management
- Partner with fraud risk teams, data engineers, IT infrastructure, and business stakeholders to deliver end-to-end fraud detection solutions that work seamlessly across the organisation
- Communicate complex model insights, performance results, and strategic recommendations clearly and confidently to senior management and non-technical business stakeholders
- Actively contribute to the fraud data science community of practice — sharing knowledge, reviewing peers’ work, and driving the team’s collective capability forward
Qualifications & Requirements
Educational Requirements
- Master’s degree or PhD in Data Science, Statistics, Computer Science, Mathematics, or a closely related quantitative field — mandatory for this senior role
- Strong academic foundation in statistical modelling, probability theory, machine learning, and algorithmic design is essential
Technical Skills
- Strong proficiency in SAS (including SAS SFD), Python, and SQL for model development, data manipulation, and production system integration
- Practical experience with machine learning frameworks including TensorFlow, PyTorch, and Scikit-learn applied to classification, anomaly detection, and fraud scoring problems
- Hands-on MLOps experience with tools and practices including MLflow, Kubeflow, and CI/CD pipeline design for model lifecycle management
- Deep understanding of card fraud detection techniques, transaction data characteristics, and the unique modelling challenges of issuing and acquiring fraud domains
Experience Requirements
- 7+ years of professional experience in data science roles with a proven track record of deploying models in live production environments
- Minimum 3 years of dedicated experience in card fraud modelling — this is a strict domain expertise requirement, not a generalised data science role
- Demonstrated experience working in the banking, financial services, or payments industry with knowledge of fraud risk management and regulatory compliance frameworks
Preferred Qualifications
- Direct experience in the banking or payments industry with exposure to issuing, acquiring, or card scheme fraud environments
- Familiarity with cloud platforms — AWS, Microsoft Azure, or Google Cloud Platform — for model hosting, data pipeline management, and scalable inference deployment
- Knowledge of regulatory compliance requirements in fraud risk management within GCC or international banking regulatory frameworks
Why Senior Fraud Data Science Roles in Dubai Are Booming in 2026
Dubai has rapidly evolved into one of the Middle East’s most dynamic financial and payments hubs, with billions of card transactions processed annually and a fintech ecosystem growing at extraordinary speed. The demand for highly skilled Fraud Data Scientists in Dubai has surged — driven by the increasing sophistication of financial crime, the UAE Central Bank’s strengthening regulatory requirements, and the pressure on banks and payment processors to deploy smarter, faster, and more accurate fraud detection systems.
For a Senior Fraud Data Scientist with card fraud expertise, Dubai offers an exceptional combination of zero personal income tax, world-class banking infrastructure, competitive senior-level salaries, and the opportunity to work on some of the most technically challenging fraud detection problems in the MENA financial services sector. If you bring the expertise, Dubai in 2026 is where your career in financial crime data science will reach its peak.
Who Should Apply?
- Card Fraud Data Scientists: With 3+ years of dedicated fraud model development experience and strong SAS or Python background
- Senior ML Engineers in Financial Services: With deep domain knowledge of transaction data, fraud patterns, and production model deployment
- MLOps-Experienced Data Scientists: Who have built automated retraining, monitoring, and CI/CD pipelines for models in live banking systems
- PhD or Master’s Graduates in Quantitative Fields: With 7+ years of applied data science experience in banking, payments, or financial crime
- UAE-Based or Relocating Data Scientists: Seeking a senior, high-impact fraud data science role in one of the Middle East’s leading financial centres
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