Senior Fraud Data Scientist
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Key skills for this role
About the Role
Dicetek LLC is seeking a Senior Fraud Data Scientist to lead the development and deployment of advanced card fraud detection models in Dubai. The role involves end-to-end model lifecycle management using SAS SFD and MLOps practices.
Key Skills for This Role
Responsibilities
- 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
- Deploy models using SAS SFD and integrate with production systems
- Establish and maintain MLOps pipelines for continuous integration, deployment, and monitoring
- Conduct rigorous model validation, stress testing, and performance benchmarking
- 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
Requirements
- 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
- 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
- Proven track record of deploying models in production environments
Full Job Posting
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.
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: 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.
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