Data Scientist II

Technical Expertise

• Strong background in machine learning, statistical modeling, predictive analytics, and feature engineering, with hands-on experience developing and deploying classical ML models.
• Strong proficiency in classical machine learning algorithms including Linear Regression, Logistic Regression, Decision Trees, Random Forest, XGBoost, LightGBM, CatBoost, Support Vector Machines (SVM), Naive Bayes, K-Means, and DBSCAN.
• Experience with feature engineering, feature selection, dimensionality reduction, hyperparameter optimization, cross-validation, model calibration, and model evaluation.
• Proficiency in Python, Pandas, NumPy, Scikit-learn, with experience using libraries such as XGBoost, LightGBM, CatBoost, and Statsmodels.

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