Data Science - AI / ML

Must-Have Technical Skills:

1. Advanced ML Models: Deep expertise in supervised and unsupervised learning algorithms including ensemble methods, tree-based models, neural networks, and classical ML techniques
2. Feature Engineering: Proven ability to create, select, and transform features for optimal model performance
3. Model Optimization: Experience with hyperparameter tuning, model selection, and performance optimization techniques
4. Statistical Rigor: Strong foundation in statistical methods, experimental design, and statistical inference
5. Data Visualization: Proficiency in creating insightful visualizations using tools like Matplotlib, Seaborn, Plotly, or Tableau
6. Hypothesis Testing: Expertise in designing and conducting statistical tests to validate hypotheses and model assumptions
7. Problem-Solving Framework: Ability to structure and approach complex analytical problems systematically
8. Python/SQL: Advanced proficiency in Python (pandas, scikit-learn, numpy, scipy) and SQL for data manipulation and analysis