Dhritiman Nandi

Pioneered the creation of a remote sensing data-based economic activity ranking system for districts using satellite images. Developed and fine-tuned large language models for generating alerts and notifications on relevant news articles, providing economists with timely insights for informed decision-making.

Developed machine learning-based models for shoot development and pest prediction and detection, utilizing weather and Remote Sensing data. Achieved an estimated improvement in yield by an impressive 30% using a model-centric approach.

Developed an LSTM model to replace existing forecasting approaches, improving MAPE from 70% to 92%. Researched feasibility of Q-learning and SARSA as replacements for 5R-based approaches in supply chain optimization.

Developed a flagship product using clinical data that recommends proper antibiotics for patients based on symptoms and attributes in real time. Managed a team of 5 data scientists and resource allocation, leading the patenting process of the product.

Implemented a variable coefficient model for empirical demand estimation at the brand level for markets where only aggregated sales data was available. This helped in calculating the demand elasticities and price elasticities across segments and competitions.

Conducted statistical inference analysis on investment impact at the pincode level. This involved analyzing data to understand the effects of investments.

Participated in the development of a fraud detection model for handling large volumes of fraudulent transactions. Monitored different components of the application and database to handle B2B card payment systems.

Managed data migration projects for clients like GSK and JPMC, reducing completion time by 80%. Resolved special character issues in clinical data migration.

Pursued a Master of Technology degree in Quality, Reliability, and Operations Research. Focused on advanced statistical methods and operational excellence.

Completed a Bachelor of Engineering in Electrical Engineering. Gained foundational knowledge in electrical systems and engineering principles.