8+ years in machine learning, 5+ years in reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, or artificial intelligence.
Expertise in classical ML techniques (e.g., Classification, Clustering, Regression) using algorithms like XGBoost, Random Forest, SVM, and KMeans, with hands-on experience in RL methods such as Contextual Bandits, Q-learning, SARSA, and Bayesian approaches for pricing optimization.
Proficiency in handling tabular data, including sparsity, cardinality analysis, standardization, and encoding.
Proficient in Python and SQL (including Window Functions, Group By, Joins, and Partitioning).
Experience with ML frameworks and libraries such as scikit-learn, TensorFlow, and PyTorch
Knowledge of controlled experimentation techniques, including causal A/B testing and multivariate testing