Senior Fraud Data Scientist

Virtusa; Dubai; On-site; AED 240,000 - 360,000; Full time

Job summary

Virtusa is seeking a Senior IC Data Scientist specializing in card fraud modeling to design and deploy advanced detection models. The role emphasizes scalable production deployments, robust validation, and close collaboration with fraud risk, IT and business teams in a dynamic environment.

The candidate will develop models using SAS SFD and Python, implement MLOps pipelines, and ensure compliance with regulatory requirements while delivering insights to senior management.

Qualifications

• Masters or Ph.D. in Data Science, Statistics, Computer Science or related field.
• Strong proficiency in SAS (including SAS SFD), Python and SQL.
• Hands-on experience with ML frameworks and MLOps tools; deep knowledge of card fraud detection.
• 7+ years in data science roles with at least 3 years in card fraud modeling; production deployments.

Responsibilities

• Develop and optimize card fraud detection models for issuing and acquiring portfolios.
• Deploy models using SAS SFD and integrate with production systems; ensure scalability.
• Establish MLOps pipelines for CI/CD, retraining and monitoring of fraud models.
• Conduct rigorous validation, stress testing and performance benchmarking.
• Collaborate with fraud risk data engineers IT and business teams; report to senior stakeholders.

Skills

Strong analytical & problem-solving

Excellent communication & stakeholder

Card fraud modeling

Education

Masters or Ph.D. in Data Science, Statistics, Computer Science or related field

Tools

SAS (including SAS SFD)

Python

SQL

TensorFlow

PyTorch

Scikit-learn

MLflow

Kubeflow

CI/CD pipelines

Job description

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 Masters 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. MLflowKubeflow CI/CD pipelines). Deep understanding of 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.

Required Experience:

Senior IC