Senior Fraud Data Scientist

Senior Fraud Data Scientist10+ Years Dubai UAE Banking or top-tier banking experience1.Work Experience (all are mandatory)Past experience in using SAS SFDBuilding fraud detection models using Machine Learning (ML)Model management and MLOPs2.Domain Knowledge: Fraud analytics for credit cards3.Key Technical Skills: SAS SFDPython & SQLMachine LearningCommunication skills: Must be strong enough to manage stakeholder requirementsPhD candidates will be preferred by clientRole PurposeThe Senior Data Scientist will lead the development, optimization, and deployment ofadvanced card fraud detection models for issuing and acquiring businesses. This roleinvolves end-to-end model lifecycle management, including training, hosting, evaluation,and deployment using SAS SFD and modern MLOps practices. The candidate will workclosely 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 cardfraud detection models for issuing and acquiring portfolios. Implement advancedstatistical, machine learning, and AI techniques to improve fraud detection accuracyand precision and minimizing false positives.● Model Hosting & Deployment: Deploy models using SAS SFD and integrate withproduction systems. Ensure seamless hosting and scalability of models acrossmultiple environments.● MLOps & Automation: Establish and maintain MLOps pipelines for continuousintegration, deployment, and monitoring of fraud models. Automate model retrainingand performance tracking processes.● Evaluation & Testing: Conduct rigorous model validation, stress testing, andperformance benchmarking. Collaborate with fraud operations teams to ensuremodels meet business and regulatory requirements.● Collaboration & Stakeholder Management: Partner with fraud risk, data engineers, ITand business teams to deliver end-to-end solutions. Communicate insights andrecommendations to senior management and business stakeholders.Required Skills & Qualifications● Education: Master’s or Ph.D. in Data Science, Statistics, Computer Science, orrelated 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 techniquesand transaction data.● Experience: 10+ years in data science roles, with at least 3 years in card fraudmodeling. Proven track record of deploying models in production environments.● Soft Skills: Strong analytical and problem-solving skills. Excellent communicationand stakeholder management abilities.Preferred Qualifications● Experience in banking or payments industry.● Familiarity with cloud platforms (AWS, Azure, GCP) for model hosting anddeployment.● Knowledge of regulatory compliance in fraud risk management.