Qualifications
• 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.
Should Have Experience:
The focused skills are Reinforcement learning, optimization techniques, pricing , Baysium , tabular ML, traditional ML and Classical AI models.
- AI-ML- Data Engineering + ML Principles
- ML/AI OOPS- Streamline
- MLOPS- Scalar pipelines, Drift detection, Model Registry, build Modular, module internal Libraries, Heary Python, Pyspark
- ML frameworks- TensorFlow, Theano, Scikit-learn, Caffe, Apache Mahout, Apache Spark, PyTorch, Amazon Sage Maker, Microsoft Cognitive Toolkit