Senior Fraud Data Scientist

Virtusa Middle East FZ-LLC; Dubai; On-site; AED 300,000 - 460,000; Full time

Job summary

Virtusa Middle East FZ-LLC invites an experienced data science leader to develop and optimize card fraud detection models for issuing and acquiring portfolios in a fast-paced banking environment. The role emphasizes deploying models with SAS SFD, building robust MLOps pipelines, and rigorous validation, with collaboration across fraud risk, IT, and data teams to deliver production-ready solutions.

Candidates should have 7+ years in data science, with at least 3 years in card fraud modeling, and

Qualifications

• Master’s or Ph.D. in Data Science, Statistics, Computer Science, or related field.
• 7+ years in data science roles, with at least 3 years in card fraud modeling.
• Proven track record of deploying models in production environments.

Responsibilities

• Card Fraud Model Development: Develop and optimize card fraud detection models for issuing and acquiring portfolios.
• Model Hosting: Deploy models using SAS SFD and integrate with production systems.
• MLOps: Establish and maintain MLOps pipelines for CI/CD and monitoring of fraud models.
• Evaluation: Conduct rigorous validation, stress testing, and performance benchmarking with fraud ops teams.
• Collaboration: Partner with fraud risk, data engineers, IT, and business teams; communicate insights to senior management.

Skills

SAS

Python

SQL

TensorFlow

PyTorch

Scikit-learn

MLflow

Kubeflow

CI/CD

Fraud modeling

Education

Master's or PhD in Data Science

Tools

MLflow

Kubeflow

CI/CD pipelines

Job description

Card Fraud Model Development

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 while minimizing false positives.

Model Hosting

Deploy models using SAS SFD and integrate with production systems. Ensure seamless hosting and scalability across multiple environments.

MLOps

Establish and maintain MLOps pipelines for continuous integration, deployment, and monitoring of fraud models. Automate model retraining and performance tracking processes.

Evaluation

Conduct rigorous model validation, stress testing, and performance benchmarking. Collaborate with fraud operations teams to ensure models meet business and regulatory requirements.

Collaboration

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.

Education

Master’s or Ph.D. in Data Science, Statistics, Computer Science, or related field.

Technical Skills

• SAS (including SAS SFD)
• Python
• SQL
• Machine learning frameworks (TensorFlow, PyTorch, Scikit-learn)
• MLOps tools and practices (MLflow, Kubeflow, CI/CD pipelines)
• Card fraud detection techniques and transaction data

Experience

7+ 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.