Senior Data Scientist(W2)

Required Skills • 8+ years of hands-on experience in Data Science, Machine Learning, and AI development. • Strong experience in building and deploying real-time ML models and data pipelines. • Proven experience with streaming platforms such as Azure Event Hub, Apache Kafka, or similar. • Expertise in cloud platforms, preferably Microsoft Azure (Azure ML, Databricks, Synapse). • Hands-on experience with Kubernetes for deploying and scaling ML models (MLOps). • Strong knowledge of NoSQL databases such as Azure Cosmos DB, MongoDB, or DynamoDB. • Proficiency in Python, along with libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy. • Experience with real-time inference systems and model serving frameworks. • Strong understanding of data modeling, feature engineering, and ML optimization techniques. • Familiarity with CI/CD pipelines for ML (MLOps), monitoring, and automated retraining. Key Responsibilities • Design, develop, and deploy machine learning models for real-time data processing and analytics. • Build and maintain streaming ML pipelines using Azure Event Hub (or Kafka-like systems). • Architect and implement scalable ML microservices deployed on Kubernetes with auto-scaling. • Develop and optimize feature stores and data pipelines integrated with NoSQL databases (Cosmos DB, MongoDB, DynamoDB). • Apply advanced techniques in feature engineering, model selection, and hyperparameter tuning. • Implement real-time model inference systems with low latency and high throughput. • Ensure production readiness by embedding monitoring, logging, drift detection, and self-healing mechanisms. • Lead model lifecycle management, including versioning, deployment, and retraining strategies. • Handle production support, including troubleshooting model performance issues and pipeline failures. • Collaborate with data engineers, DevOps, and cloud architects to ensure seamless integration and scalability. • Drive adoption of best practices in AI/ML, MLOps, and responsible AI.