Data Science/MLOps Engineer– Retail/CPG Domain

We are seeking a highly skilled and motivated Data Science & MLOps Engineer to join our growing team. This role combines expertise in traditional machine learning, model deployment, and scalable MLOps infrastructure to drive business impact in the Retail and Consumer Packaged Goods (CPG) domain. The ideal candidate will have a strong background in building and operationalizing end-to-end ML pipelines and delivering production-grade ML solutions aligned with real-world use cases such as demand forecasting, pricing optimization, churn prediction, and personalization. Key Responsibilities: Design and develop robust machine learning models for a variety of use cases including prediction, classification, segmentation, and time-series forecasting. Build, automate, and monitor ML pipelines using tools such as MLflow, Kubeflow, Airflow, Metaflow, or equivalent. Collaborate with data engineers and domain experts to transform business problems into scalable ML solutions. Deploy models into production environments using containerization (Docker) and orchestration frameworks (Kubernetes, AWS SageMaker, Azure ML, GCP Vertex AI). Set up and maintain model versioning, tracking, retraining workflows, and CI/CD pipelines for ML systems. Implement testing, monitoring, and performance tracking for deployed models to ensure reliability, fairness, and accuracy. Collaborate with DevOps and platform teams to improve infrastructure, scalability, and cost-efficiency of ML workloads. Stay updated on developments in MLOps, traditional ML algorithms, and best practices for production machine learning.

EXL is a leading analytics and digital solutions company that partners with clients to improve business outcomes and unlock growth. By bringing together deep domain expertise with innovative technologies and data science, we solve complex business problems for clients across multiple industries.