Data Scientist

Senior Data Scientist ML & MLOps

Experience: 6+ Years

Locations: Gurugram | Pune | Bengaluru | Hyderabad | Chennai | Bhopal | Jaipur

Work Mode: Hybrid 2–3 Days from Office

Shift: 12:00 PM – 9:00 PM IST

Role Overview

We are looking for a Senior Data Scientist with a strong engineering and machine learning background to build, deploy, and operationalize production-grade ML solutions.

The role involves the complete ML lifecycle — from data exploration, feature engineering, model development and training to production inference, monitoring, and model lifecycle management.

You will work with a modern GCP and Kubernetes-based ML platform, using technologies such as Python, Vertex AI, Kubernetes, and Argo Workflows.

Key Responsibilities

Data Science & Model Development

• Develop, train, validate, and optimize machine learning models using Python.
• Perform EDA, feature engineering, model selection, and performance evaluation.
• Apply appropriate ML techniques for prediction, classification, optimization, and other business use cases.
• Work with ML frameworks such as scikit-learn, PyTorch, CatBoost, or similar.ML Pipeline & Workflow Orchestration
• Design and implement automated ML workflows using Argo Workflows on Kubernetes.
• Build repeatable pipelines for model training, inference, and post-processing.
• Ensure ML workflows are scalable, reliable, and production-ready.Model Training & Inference
• Use Google Vertex AI for scalable model training, validation, and hyperparameter tuning.
• Develop and maintain Python-based training, inference, and feature-engineering runtimes.
• Optimize inference pipelines for performance, scalability, and reliability.
• Support both batch and real-time inference use cases.
• Monitor and troubleshoot production ML pipelines.
• Participate in on-call support for inference infrastructure.Model Lifecycle & Storage
• Manage trained model artifacts using Google Cloud Storage (GCS).
• Maintain model versions, metadata, lineage, and related artifacts.
• Support model versioning, rollback, reproducibility, and auditability.
• Work with model registries or custom model-management solutions.Monitoring, Quality & Governance
• Define appropriate model evaluation metrics and validation criteria.
• Support production monitoring, model drift detection, performance monitoring, and retraining.
• Implement best practices around ML testing, documentation, reproducibility, and responsible AI.
• Collaborate with engineering and platform teams to ensure reliable ML operations.

Required Skills

Must-Have

• 6+ years of hands-on experience in Data Science / Machine Learning.
• Strong programming skills in Python.
• Strong understanding of machine learning, statistics, feature engineering, model evaluation, and validation.
• Hands-on experience with ML frameworks such as:
• scikit-learn
• PyTorch
• CatBoost or similar
• Experience taking ML models from experimentation to production.
• Practical understanding of MLOps and ML lifecycle management.
• Hands-on exposure to Kubernetes.
• Experience with Argo Workflows or Kubernetes-based workflow orchestration.
• Experience with Google Cloud Platform (GCP) and preferably Vertex AI.

Good to Have

• Experience with real-time or batch inference systems.
• Experience with ML CI/CD pipelines.
• Knowledge of model monitoring and drift detection.
• Experience implementing automated model retraining strategies.
• Exposure to model registries, metadata management, and model lineage.