Lead Data Scientist

Role & responsibilities

• Lead the design, development, and deployment of end-to-end data science and machine learning solutions.
• Strong hands-on experience in Python and data science libraries such as Pandas, NumPy, Scikit-learn, PyTorch, and TensorFlow.
• Design and implement machine learning models for classification, regression, clustering, forecasting, recommendation, and NLP use cases.
• Strong understanding of statistics, probability, hypothesis testing, experimentation, feature engineering, and model evaluation.
• Experience with Generative AI, Large Language Models (LLMs), Prompt Engineering, RAG, embeddings, vector databases, and AI agents.
• Experience building and deploying production-grade AI/ML solutions using cloud platforms such as Azure or AWS.
• Hands-on experience with Azure AI Foundry / Azure OpenAI / Amazon SageMaker or equivalent AI/ML platforms.
• Experience designing data pipelines and ML pipelines for training, validation, deployment, and monitoring.
• Experience with MLOps, including model versioning, experiment tracking, CI/CD, model deployment, monitoring, and model drift detection.
• Strong experience working with SQL and relational databases and familiarity with data warehouses/data lakes.
• Experience with REST APIs, microservices, Docker, and Kubernetes for AI/ML application deployment.
• Implement AI/ML solutions with appropriate security, privacy, responsible AI, and governance controls.
• Optimize models for accuracy, scalability, latency, reliability, and cost.
• Lead technical discussions and mentor Data Scientists, ML Engineers, and junior team members.