Python (including Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, and PySpark)
SQL (writing complex queries, stored procedures, and data extraction)
Machine learning techniques (supervised and unsupervised learning, reinforcement learning, and causal inference)
Building, training, and deploying machine learning models (model pipelines, feature engineering, hyperparameter tuning, and model explainability)
Cloud-based data and AI/ML platforms (e.g., Azure, AWS, or GCP), including deploying and managing models in production environments
Power BI, Tableau, or similar platforms