Data Scientist - Banking Risk Models, MLOps & AWS SageMaker in banking domain

We have an urgent requirement for Data Scientist - Banking Risk Models, MLOps & AWS SageMaker  in banking domain is required for our banking clients in Abu Dhabi ,UAE

Strong ML Engineering & MLOps expertise.--MustExtensive AWS SageMaker experience--Must.Experience with Risk, SME, Retail Banking, Propensity, and Churn Models--MustCapable of taking ML solutions from experimentation to enterprise-scale production deployment--MustBanking Domain --Must

Key Responsibilities

Machine Learning & Model Development

• Design, develop, validate, and deploy advanced machine learning models for banking use cases.


• Build and enhance:




• Credit Risk Models


• Business Banking Models


• SME (Small & Medium Enterprise) Models


• Retail Banking Models


• Propensity Models


• Customer Churn Prediction Models




• Implement feature engineering, model selection, hyperparameter tuning, and performance evaluation.


• Translate business requirements into scalable AI/ML solutions.

ML Engineering & MLOps

• Build end-to-end ML pipelines from data ingestion to production deployment.


• Implement MLOps best practices including CI/CD, model versioning, experiment tracking, model monitoring, and automated retraining.


• Develop scalable and reusable ML frameworks for enterprise adoption.


• Monitor model performance and address model drift, bias, and data quality issues.

AWS & SageMaker

• Design and deploy ML solutions using AWS ecosystem.


• Build and manage machine learning workflows using:




• AWS SageMaker


• S3


• Lambda


• Glue


• Redshift


• RDS


• Step Functions


• IAM


• CloudWatch




• Optimize cloud infrastructure for performance, scalability, and cost efficiency.

Banking Domain & Analytics

• Work with large-scale banking and financial datasets.


• Partner with Risk, Compliance, Fraud, Business Banking, Retail Banking, and Analytics teams.


• Ensure adherence to banking regulations, data governance, model risk management, and data security standards.


• Deliver actionable insights and predictive solutions supporting business growth and risk mitigation.

Required QualificationsExperience

• Minimum 7+ years of experience in Data Science, Machine Learning Engineering, or Advanced Analytics.


• 4+ years of hands-on experience in ML Engineering and MLOps.


• Proven experience in Banking or Financial Services domain is mandatory.

Technical Skills

• Strong expertise in:




• Python


• Machine Learning


• ML Engineering


• MLOps


• AWS SageMaker


• AWS Cloud Services


• SQL


• PySpark




• Experience with:




• Model Deployment & Monitoring


• CI/CD Pipelines


• Docker


• Kubernetes (preferred)


• Git


• Airflow


• Feature Stores


• Experiment Tracking Tools

Banking Model Experience (Mandatory)Candidate should have hands-on experience developing or managing at least several of the following:

• Risk Models


• Credit Scoring Models


• Business Banking Models


• SME Models


• Retail Banking Models


• Propensity Models


• Churn Prediction Models


• Customer Segmentation Models

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• Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related quantitative field.