Microsoft

Worked on Deepask multilingual natural question generation models in Bing supporting 100+ languages. 
Have worked on end to end process from building in house datasets ,developing models to deploying in production. Worked on ”FAST” streamline pipeline design. Developed back-end pipeline for bulk offline data generation. Refactored the backend pipeline to make it faster, agile, robust and more resilient to failures. This has reduced pipeline latency by two third(15 to 5 days).

Applied Scientist ll

I was part of  Bing Retail Catalog team, where I had worked on bringing various experiences like seller grouping, filters etc on bing shopping vertical.This had enhanced user engagement. 
I had worked on building ads conflation models for conflating product with product ads. I had worked on modularizing ads pipeline thus making it more robust , efficient and scalable to deal with hundred of gigabytes of data going to production. The pipeline had been made agnostic enough to handle new markets .

 Data  and Applied Scientist

IITB

Teaching assistant for CS 725:Foundation of Machine learning ,CS 213:Data Structure and Algorithm, CS 101:Computer programming and utilization

Teaching Assistant

I'm experienced working into different aspects of Data Science over the last 5 years. My interests include but not limited to pre-training of language models and natural language generation. 

In my current role, I've been contributing towards Generating  natural and concise questions from documents for search suggestions (People Also Ask feature)in Bing. I have also contributed towards building data pipeline for streaming and batch processing of data. I work throughout the complete lifecycle of deep learning problems starting from planning, ideation, data processing, development and deployment.

Prior to this , I have worked in Bing Shopping team.  I have applied machine learning to add features like multiple seller, filters to L2 shopping page.  I owned and maintained ads conflation pipeline  responsible for stamping ads with catalogue products.

I have following Technical Skills:
 1. Programming Language(s): Python, C#
 2. Data Engineering and Machine Learning: Pandas, Numpy, Scikit-Learn, SQL
 3. Deep Learning: PyTorch, Tensorflow, Keras, NLP

I'm looking for roles related to Data Science and Machine Learning. If you've relevant opportunities, please reach out to me.

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