-Provided expert analysis for clients through ad-hoc requests, utilizing Pyspark on GCP and Databricks for efficient data processing.-Collaborated with a client in OBGYN/Women's Health sector to predict HCP growth, built Databricks pipelines for feature creation and model deployment, developed classification models using various datasets, and applied K-means clustering & RNN for segmentation to aid sales targeting.-Experience in working with different teams to create POCs on Dynamic Marketing, Lead generation analysis, Alerts Generation, Market Mix Modelling, Probabilistic Graphical Modelling, LSTM-Managed end-to-end projects encompassing exploratory data analysis, statistical modeling, and report creation to align with business objectives. Leveraged SQL, Python, R, and Pyspark for comprehensive data analysis, including managing and processing large datasets with Hadoop and Spark.

-Developed machine learning models tailored to client needs for data categorization using NLP processes-Employed algorithms such as XGBoost, Random Forest Classification, and K-Means clustering to optimize ticket closure processes.-Constantly working to increase the accuracy of models by trying out various techniques like Cross Validation, Over and Under Sampling, SMOTE,Transformers, etc-Created a churn prediction model for a telecom operator using Decision Tree and Random Forest, with deployment via Flask API and UI. Employed Seaborn and Plotly for visualization, and XGBoost for shipwreck survival prediction.-Developed a pipeline for System performance anomaly detection using ElasgticSearch, Logstash and Kibana.