Description

You will build and operationalize predictive models across multiple business lines to drive fraud reduction and operational efficiency.

This role is on-site.

Responsibilities

• Develop predictive models using structured and unstructured data.
• Build and maintain ML pipelines for automated training, testing, deployment, and monitoring in cloud environments.
• Implement CI/CD workflows for model versioning, retraining, and lifecycle management.
• Monitor model performance using observability tools and enforce governance standards.
• Collaborate with engineering teams to provision containerized environments and support low-latency scoring APIs.

Required Skills

• 10+ years of experience in a data science or relevant engineering role.
• Strong proficiency in Python and SQL.
• Experience with ML libraries such as TensorFlow and PyTorch.
• Hands-on experience with GCP, AWS, and Azure cloud platforms.
• Proficiency in containerization technologies, specifically Docker and Kubernetes.
• Familiarity with data engineering tools, including Airflow and Spark.
• Experience implementing MLOps frameworks like MLflow.

Preferred Skills

• Experience with low-latency model scoring APIs.
• Background in fraud detection or operational efficiency projects.

Key Skills

Education

Any Gradute