Data Scientist

Senior Fraud Data Scientist

10+ Years

Dubai

UAE Banking or top-tier banking experience

1.Work Experience (all are mandatory)

Past experience in using SAS SFD

Building fraud detection models using Machine Learning (ML)

Model management and MLOPs

2.Domain Knowledge: Fraud analytics for credit cards

3.Key Technical Skills:

• SAS SFD
• Python & SQL
• Machine Learning

1. Communication skills: Must be strong enough to manage stakeholder requirements
2. PhD candidates will be preferred by client

Role Purpose

The Senior Data Scientist will lead the development, optimization, and deployment of

advanced card fraud detection models for issuing and acquiring businesses. This role

involves end-to-end model lifecycle management, including training, hosting, evaluation,

and deployment using SAS SFD and modern MLOps practices. The candidate will work

closely with fraud risk, data engineering, and technology teams to ensure robust,

scalable, and high-performing solutions.

Key Responsibilities

● Card Fraud Model Development & Optimization: Design, develop, and optimize card

fraud detection models for issuing and acquiring portfolios. Implement advanced

statistical, machine learning, and AI techniques to improve fraud detection accuracy

and precision and minimizing false positives.

● Model Hosting & Deployment: Deploy models using SAS SFD and integrate with

production systems. Ensure seamless hosting and scalability of models across

multiple environments.

● MLOps & Automation: Establish and maintain MLOps pipelines for continuous

integration, deployment, and monitoring of fraud models. Automate model retraining

and performance tracking processes.

● Evaluation & Testing: Conduct rigorous model validation, stress testing, and

performance benchmarking. Collaborate with fraud operations teams to ensure

models meet business and regulatory requirements.

● Collaboration & Stakeholder Management: Partner with fraud risk, data engineers, IT

and business teams to deliver end-to-end solutions. Communicate insights and

recommendations to senior management and business stakeholders.

Required Skills & Qualifications

● Education: Master’s or Ph.D. in Data Science, Statistics, Computer Science, or

related field.

● Technical Skills: Strong proficiency in SAS (including SAS SFD), Python, and SQL.

Experience with machine learning frameworks (e.g., TensorFlow, PyTorch,

Scikit-learn). Hands-on experience with MLOps tools and practices (e.g., MLflow,

Kubeflow, CI/CD pipelines). Deep understanding of card fraud detection techniques

and transaction data.

● Experience: 10+ years in data science roles, with at least 3 years in card fraud

modeling. Proven track record of deploying models in production environments.

● Soft Skills: Strong analytical and problem-solving skills. Excellent communication

and stakeholder management abilities.

Preferred Qualifications

● Experience in banking or payments industry.

● Familiarity with cloud platforms (AWS, Azure, GCP) for model hosting and

deployment.

● Knowledge of regulatory compliance in fraud risk management.