Certifications · Bachelor’s or Master’s degree in data science, computer science, statistics or a related technical discipline · ML/AI certifications (e.g., TensorFlow, AWS SageMaker, Databricks) are preferred

Required Experience · 4–7 years in ML or data science roles within enterprise, shared services, or GCC setups · Experience in working with ML models such as recommendation engines, demand forecasting, classification, regression, and NLP

Demonstrated experience deploying ML models in production environments · Strong understanding of feature engineering, model evaluation, and bias testing · Experience with Python, Spark, SQL, and model-serving frameworks

Essential skills

· Proficiency in Python ML stacks (scikit-learn, TensorFlow, PyTorch), Spark MLlib and end-to-end ML workflows · Ability to design and deploy scalable ML Models on a major cloud platform · Competence in model evaluation techniques and performance tuning · Experience creating unit tests, CI/CD pipelines, and code reviews

Desired skills · Hands-on experience with Google Cloud Platform (GCP) is strongly preferred · Working experience with data warehouse environments (e.g., Snowflake) and analytics tools (e.g., Tableau) · Domain expertise in retail, supply chain, pricing, or customer analytics · Experience with enterprise-scale deployments (Snowflake, Tableau) · Cloud certifications (AWS, GCP, Azure) or proficiency in Docker, Kubernetes · Familiarity with GenAI, LLMs or RAG frameworks

ROLES & RESPONSIBILITIES

Delivery and

Execution · Partner with product and data teams to convert business questions into ML use cases · Design feature pipelines and define model evaluation criteria aligned with platform standards · Build ML pipelines for training and inference, ensuring scalability and performance · Implement rigorous testing, validation and monitoring for ML lifecycle · Apply cross-validation, hyperparameter tuning and explainable AI approaches · Convert models developed by data scientists into production-ready code · Package and deploy ML models via microservices or serverless environments and deploy across environments

Support and

Enablement · Write modular, documented code according to architecture standards and participate in peer reviews · Troubleshoot model behaviour in production and improve performance iteratively · Collaborate with MLOps engineers for environment provisioning and deployment

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