 Developed and implemented robust algorithms for fraud detection, utilizing advanced techniques such asword segmentation, part-of-speech tagging, and NLP to effectively identify and mitigate fraudulent activitieswithin telecommunications data. Applied clustering algorithms, specifically K-means, to group customers and strategically implemented datadriven decisions on promotional offers and pricing strategies, resulting in a significant reduction in customerchurn. Built and deployed sophisticated time-series models for financial data, demonstrating proficiency inrecognizing complex patterns and providing accurate forecasts, contributing to informed decision-making. Led the implementation of sentiment analysis using NLP models, including LSTM cells in RNNs, on customeremail feedback and reviews. Successfully determined emotional tones, attitudes, and sentiments to enhancecustomer experience strategies. Developed and applied a range of machine learning algorithms, including linear regression, logisticregression, multiple regression, Naïve Bayes, and Random Forests. This diverse skill set showcasesadaptability and versatility in solving various data science challenges. Led end-to-end data mining processes, including data collection, cleaning, manipulation, model development,validation, and visualization. Demonstrated an ability to extract valuable insights from diverse datasets. Applied advanced techniques such as Principal Component Analysis (PCA) and clustering models forstructured and unstructured datasets, showcasing expertise in dimensionality reduction for enhanced modelaccuracy. Implemented cross-validation techniques and loss functions for rigorous model validation. Created ConfusionMatrix, ROC, and CAP curves to ensure the reliability and robustness of machine learning models.

Developed a machine learning system that predicted purchase probability through offers based oncustomer's real-time location data and past purchase behavior. These predictions are being used for mobilecoupon pushes. Developed a model that collects data across thousands of locations to optimize product placement andadvertising to catch the eye of shoppers that fit the right profile and selection of products to be removedentirely to reduce clutter and make it easier to find in-demand items automatically. Participated in developing deep learning model for the process of comparing items against each other,tracked their performance in various situations & made suggestions to support key business decisions. Built one class Support Vector Machine (SVM) and Principal Component Analysis (PCA) algorithms foranomaly detection of fraud and other errors that signal dishonest behaviors. Forecasted sales and improved accuracy - (MAPE and RMSE) by 30% by implementing advancedforecasting algorithms that were effective in detecting seasonality and trends in the patterns. In addition to incorporating exogenous covariates, increased accuracy of the machine learning algorithmshelped business plan better with respect to budgeting and sales and operations planning. Used python to build machine learning algorithms by importing Sci-kit learn, SciPy, NumPy, Pandas modulesto analyze the terabytes of data. Developed QlikView and QlikSense objects like Multi Boxes, Straight tables, Pivot tables, Containers, Linecharts, Bar charts, Combo charts, Scatter charts, Line objects, Pie charts, Buttons.