Senior Fraud Data Scientist
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Key skills for this role
About the Role
DICETEK LLC is seeking a Senior Data Scientist to lead the development and deployment of advanced card fraud detection models. 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
- Deploy models using SAS SFD and integrate with production systems
- Establish and maintain MLOps pipelines for continuous integration, deployment, and monitoring of fraud models
- 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
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
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.
- Model Hosting & Deployment: Deploy models using SAS SFD and integrate with production systems.
- MLOps & Automation: Establish and maintain MLOps pipelines for continuous integration, deployment, and monitoring of fraud models.
- Evaluation & Testing: Conduct rigorous model validation, stress testing, and performance benchmarking.
- Collaboration & Stakeholder Management: Partner with fraud risk, data engineers, IT and business teams to deliver end to end solutions.
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: 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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