Associate Data Scientist

Cooee® - Full Time - May, 2021 : till date

• Working on hyper-personalisation domain

• Using Genetic Algorithm to dynamically action text and call-action-button on images

• Improved user retention for stakeholders by 4.5% sending personalised notifications using reinforcement learning

• Generation of unique notification image payload to increase user engagement with the help of genetic algorithms

• Faster payload generation by 10% via improved database optimized queries

• Exploration on user recency and RFM

Associate Data Scientist

Riverus - Full Time - June, 2019 : May, 2021 - 2 years

• Extraction of high value analytics from Legal Corpus

• Building robust ML pipelines and providing concurrent API end points

• Improving speed of text-based captcha solving by using SOTA object detection

• Improvising fuzzy text matching to be 25% faster on large scale text matching on quoted text from two legal cases

• Building case summarization tool of relative chronology of case proceedings using SOTA transformers based models

• Building a first person reasoning model to identify the polarity of speaker

• Extraction of common provisions, clauses, and data points from contracts

• Building, scaling and deploying an ensemble model of transformer and supervised machine learning algorithms to extract

information in form of one-class classification

• Improving in-house API to serve multiple requests and made it faster by 10%

• Built a sentence similarity model to find similar text wrt. context of the text using SOTA NLU techniques

Data Science Intern

KG LLP - Internship - August, 2018 : September, 2018 - 2 Months

• Developing Deep Learning models using LSTM, GRU and xgboost to predict the rise and fall of NIFTY.

• Working on high frequency time series data to automate the data gathering and prediction.

• Developing barriers for uncertainty quantification to external noises.

• Tech Stack - Python, Deep Learning, Bayesian Techniques

Project Intern

Intel Indexer LLC - Internship - Feb, 2018 : May, 2018 - 5 Months

• Process economic/finance domain algorithms and methods for feature extraction.

• Use LSTM, Dynamic Time Warping, Clustering and scalable methods to build prediction system.

• Create a GDP prediction system with 5% more accurate than IMF.

• Modeling uncertainty quantification using Bayesian methods to give the uncertain quantity for one phase of prediction.

• Tech Stack - Python, Machine Learning, Deep Learning, DTW