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Senior Fraud Data Scientist

HireAlpha
, UAE
Full Time
Senior
SAS SFDPythonSQLMachine LearningMLOpsTensorFlow
Free

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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 dete
  • 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.

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