Proficiency in Python, R, and software engineering practices for building scalable ML systems and production-ready code.

Strong foundation in statistics, linear algebra, calculus, and probability theory essential for understanding ML algorithms.

Deep understanding of various ML algorithms, when to use them, and how to optimize their performance for different problems.

Expertise in popular ML frameworks like TensorFlow, PyTorch, Scikit-learn for efficient model development and deployment.

Skills in data cleaning, preprocessing, feature engineering, and working with large datasets using tools like Pandas and Spark.

Knowledge of cloud platforms and MLOps practices for deploying, monitoring, and maintaining ML models in production.

Expertise in evaluating model performance, hyperparameter tuning, and optimization techniques to improve accuracy and efficiency.

Understanding business context and domain-specific knowledge to translate real-world problems into ML solutions.

Ability to visualize data insights and communicate complex ML concepts to technical and non-technical stakeholders.

Understanding of AI ethics, bias detection, model interpretability, and responsible AI practices for fair and transparent systems.