About

Aman Khatri is an experienced Senior Data Scientist with a demonstrated history of working in the Internet, manufacturing, and Fintech industry. With over 7.79 years of relevant experience, Aman has expertise in programming languages such as Python, R, SQL, Hive, VBA, and C. He is also proficient in visualization tools such as Power BI, MicroStrategy, Tableau, Excel Dashboards, and Google Analytics. Additionally, Aman has extensive knowledge of machine learning techniques, including Linear Regression, Logistic Regression, Classification and Regression Tree, Bagging, Boosting, KNN, Cross-Validation, Bootstrap, LAASO, Time Series Forecasting, CNN, RNN, LSTM, OCR, NLP, and Clustering. Currently, Aman is working as a Senior Data Scientist - Underwriting at Home Credit India. He is responsible for creating advanced models and solutions to support and automate customer underwriting and decision-making processes. Some of his key projects include Income Prediction model using geo-spatial data to define customer eligibility, Direct Debit Mandate payment history based delinquency score leading to 3% lift in General Model performance, and Geo Information based delinquency model using K-NN algorithm. Prior to this, Aman worked as a Data Scientist-Big Data and Data Scientist- CRM Analytics at Home Credit India. In these roles, he was responsible for development, implementation, and monitoring of credit scorecards based on traditional and alternate data sources, optimized Oracle Data Warehouse, and created sales attribution models for channel efficiency tracking. Aman has a Bachelor of Technology (B.Tech.) in Mechanical Engineering from IIT Kanpur and has completed his Class 12 and 10th from D.A.V. Century Public School. He is proficient in a wide range of tech stacks, including Research Scientist, Python, SQL, NLP, ML, ETL, CNN, RNN, XGBoost, Snowflake, BERT, Apache Hive, Bag of Words, Deep Learning, Text Classification, DeFi, Keras, Computer Vision, Seaborn, Corpus, OpenCV, Scikit, Big Data, test, TensorFlow, Pandas, MySQL, Azure, and Integration.