1) Enhanced pricing methodology of Auto and RV parts and enabled best discount allocation to customers through the implementation of various data mining techniques.2) Derived critical business KPIs by implementing statistical methods in the sales and inventory data.3) Implemented Machine Learning models for predictive analysis to forecast sales and margins and helped execute feasible promotions in KAO e-commerce sites. 4) Developed data-driven apps resulting in 200+ Man Days of savings and margin increment by 350 BPS.5) Automated existing manual processes and reports to increase the team's overall efficiency by 80%.6) Developed Python codes to extract data from unstructured data sources for internal price analysis.7) Implemented award-winning 3 Kaizen projects: Pre-Promotion Feasibility Analysis, Write-In Request Review, Line Discount Review, and 1 breakthrough project- VCPN Match Automation.

1) Developed test automation scripts for app compatibility check with the newer builds of Windows OS and identify regressions.2) Implemented new features in the test automation framework and optimized VSIX automation test automation development template. 3) Used SQL and Python to extract and transform airline industry data, for further use by the BI team to generate valuable insights.4) Worked on application maintenance projects using HTML, CSS, JavaScript, C#, etc., as a part of the L4 support team.5) Trainer to new team members and act as one of the Code Reviewers.

=>Airline Passenger Referral Prediction:1) Developed a stacked binary classifier model using base classifiers such as XGBoost, LinearSVC, Random Forest model to predict whether a passenger would recommend an airline to an acquaintance and achieved a precision of 86% on test data. =>Nifty50 Closing Price Prediction:1) Developed a time series forecasting model using ARIMA, ARIMAX, and FacebookProphet to predict the stock closing price for the next day and achieved MAPE of 11%, 8.62%, and 8.45% for the three models respectively on the test data.2) Developed a stacked binary classification model using LGB and XGBoost to predict stock price directionality and achieved a recall of 82% on the test data.=>Product Recommender System:1) Developed a product recommendation system using collaborative and content-basedfiltering by utilizing the description of products/items consumed by users.2) Used TF-IDF vectorizer on texts to obtain user-item interactions and employed cosine distance as a similarity metric to measure user-item and item-item similarities.3) Created profiles for top active users by leveraging interaction strength with therecommended items and achieved test recall@5 of 83% and recall@10 of 86%.4) Obtained an improvement in the efficiency of the user recommendation engine by 27% which will help a company to increase average order value and increase its revenue.

Student Representative/Brand Ambassador at NIT Manipur. Roles included viz. Conducting Workshops to encourage fellow students to apply for internships in the Internshala platform so that it can hone their skills and be ready for placement.

Implementation of IEEE 802.11 ah Wireless Network for unsaturated traffic. Worked on MATLAB and executed simulations that analyze the behaviour of 802.11ah network behaviour.