Apache Airflow, Git, Power Bi, Pyspark, XGBoost, Python, Sql, scikit-learn, lgbm

lifecycle management , optimization models , Sql, MLops, Python, Agent-based systems, Prompt engineering, Vector databases, Statistics, reinforcement learning, Business Strategy Analytics, ML pipelines, experimentation, Deployment monitoring, LLM GenAI, Model evaluation

Pandas, Python, Sql, scikit-learn, gradient-boosted trees, feature engineering

Sql, Git, Pandas, Numpy, Docker, XGBoost, Python, large tables, scikit-learn, window functions, PRECISION, LightGBM, Debugged latency issues optimized inference, Classification models, Auc, Deployed real-time ML models behind an API, recall, Production ML, Monitoring observability for ML systems, CTEs

Decision Trees, Python, Sql, Logistic Regression, credit risk metrics, predictive models, R, random forests, statistical and machine learning techniques, credit scoring risk models

Python, Machine Learning, Data Analysis, Statistics, R

Hadoop, Sql, Tensorflow, Pytorch, Gcp, XGBoost, Spark, Azure, Python, AWS, scikit-learn, R

Machine Learning, Apis, Containers, Big Data, Iot, Deep Learning, Tensorflow, Gcp, Pytorch, Rest Apis, Azure, Python, AWS, GeoAI, Scikit-learn, Statistics, Model Deployment

containerization , Unit Testing, Tensorflow, Numpy, Nlp, Pytorch, Python, Machine Learning, Data Transformation, Api Development, Sql, Git, Data Quality, Pandas, Computer Vision, MLops, Spark, Clustering, Airflow, data pipelines, scikit-learn, multilingual NLP, MLflow, Statistical Modeling, graph analytics, LLMs, code reviews, tree-based algorithms, feature engineering

Tensorflow, Sklearn, Nltk, Natural Language Processing, Python, Sql, Deep Learning, R