Tanmay Chimurkar

1) Working on reinforcement learning models to create navigational strategies in global financial markets. Responsibilities include conceptualizing new mathematical motifs to map their consequences in the financial markets map and validating their performance via rigorous backtesting 2) Creating a prediction engine from the financial market map that will help take concrete trading decisions based on the navigational map created by the reinforcement learning algorithm

1) Joined the ongoing project of analyzing the sentiments of S&P 500 companies during the pre and post covid era using Twitter feeds and Financial reports of the companies. The main analysis was done using NLP, bringing additional analysis ideas to the table. 2) Also took part in the analysis of user sentiments using Audio and Video data to extract and analyze the sentiments of the users.

1) Supervised a team of 2 to develop a Neural Ranking Algorithm, improving customer engagement by 43%. 2) Built a Neural Network model with the telemarketing team to improve customer lead filtering by 14% and revenue by 4.7%. 3) Collaborated to develop a monitoring dashboard in Dash with the Data engineering team, and extrapolated the use case for the Data Science team

1) Predict the number of orders, to be prepared in rush hours on daily basis. The main initiative behind the projects was to avoid frequent shut down during rush hours. 2) First-ever recommendation engine in the food industry. Analyzed item-item, and user-user collaborative filterings to make a similarity matrix, using cosine similarity as a metric. 41% of the customers added recommended items to their final order cart, thus increasing the average revenue per order by 34%.