About

Tharangini is an Advanced Data Scientist with over 11 years of industry experience in implementing predictive modeling, machine learning, and classical statistical techniques across various domains such as banking, retail, e-commerce, healthcare, and high throughput technologies like microarrays. She holds a post-graduate degree in Statistics with research experience in machine learning algorithms from the National Institute of Mental Health and Neuro Sciences (NIMHANS). For the past four years, Tharangini has been training and mentoring data science enthusiasts in data science/analytics/statistical concepts and trending tools such as R/Python in association with top analytics training institutions. She is skilled in image processing and text extraction from images and PDFs using techniques like Hough transformation, East Text Detection, OpenCV, pytesseract, and pyocr. Tharangini has also implemented predictive modeling algorithms and cluster analysis in credit scoring/risk scorecard, attrition, churn prediction, loss forecasting, survival, and recurrence prediction in healthcare. Tharangini has expertise in customer segmentation, customer lifetime value analysis, cohort analysis, retention, and attrition analysis, market basket analysis, association rule mining, and sequential pattern mining in the retail and e-commerce domain. She is proficient in statistical data analysis on psychological studies and hospital management. Tharangini has advanced-level R programming skills with good expertise in visual analytics using ggplot2. She also has intermediate-level Python skills with exposure in web scraping using Beautiful Soup and statistical analysis using SPSS. Currently, Tharangini is working as an Advanced Data Scientist at Honeywell Technology Solutions Lab Private Limited, where she is developing and driving AI/ML-based hybrid modeling approaches for Honeywell product categorization in SAP HANA Material Masters database. She is also involved in computer vision and deep learning-based projects to extract and process images for a global initiative. Tharangini has designed and developed advanced analytics and AI/ML solutions for helping businesses improve the data quality of SAP Material Masters data. She has also designed ML-based automated approaches to derive data quality accuracy rules and works closely with SAP functional leads to understand different Masters and design solutions that help improve current processes. In her past roles, Tharangini has worked as a Lead Data Scientist at Happiest Minds, where she worked on image and text processing for text detection from images and PDFs using tools such as OpenCV and Pytesseract. She also developed time series and machine learning-based models for predicting failure of industrial components. Thar