Work Experience

Microsoft Corporation is an American multinational technology company headquartered in Redmond, Washington and develops, licenses, and sells computer software, consumer electronics, and personal computers. Microsoft is best known for its Windows operating system and Office productivity suite, but the company also produces a wide range of other software products, including cloud services and gaming consoles. With a market value of over $2 trillion, Microsoft is one of the world's largest and most valuable companies.

Senior Data and Applied Scientist

May 2022 - Present

Office is the M365 organization at Microsoft.

I am a key stakeholder for the Data Science, Engineering and Product teams of Powerpoint and Graphics.
Built High Scale Content Management Platform which serves 1 Billion content across multiple Microsoft products including Word, Excel, PowerPoint, Teams, SharePoint.
Set up guidelines and best practices for deploying Machine Learning models in production on the cloud and to integrate Python based model with C# based applications.
Developed and implemented Lean Product Development methodologies.
Led the concept development and delivery of a Semantic Search Engine for Content service to access Image base content. This product improved on the relevancy of the search results by 85%.
Led the innovation and product roadmap for the delivery of a new solution that uses state-of-the-art NLP ML LLM models. Using Indexing and Embedding techniques brought down the response time by 80% and reduced space requirement by 97% to store the embeddings and index.
Saved 800K USD annually by using multilingual model which infers the language and doesn't require any manual translation of the image description based on the locales.
Built Transformers based Computer Vision models to recommend images and design ideas based on content of Slides which led in increase of Stock content use by 95%.
Integrating Dall-E, ChatGPT and Stable diffusion to provide Image editing and enhancement capability to users in Office wide products.
Interviewed, Hired and set up a specialized science and engineering team with Applied Scientists and Data Scientists.

Gartner, Inc. (NYSE: IT), is the world’s leading research and advisory company and a member of the S&P 500. Gartner equips business leaders with indispensable insights, advice and tools to achieve their mission-critical priorities and build immensely successful organizations of tomorrow.

Lead Data Scientist

May 2019 - May 2022

Gartner is an Enterprise Research and Advisory company. Gartner’s core offerings are Enterprise Market Research, Insights and Recommendations to CXOs such as Magic Quadrants and Hype Cycles. I led a cross functional team of geographically distributed data scientists, data engineers and software engineers for web and SaaS application development. My product portfolio consisted of 2 applications which serve 20,000 premium clients (CxO Suite) and 6000 internal associates.

Led a team to develop cloud based, decision support systems that enable the Service teams for customer retention.
Designed and developed Natural Language Processing (NLP) based applications to extract insights like market intelligence from public data sources such as 10K documents, Earnings Call Transcripts, SEC filings, News, etc.
Designed a Recommendation System to provide Gartner users with a highly personalized feed of recommended items on their home screen and on the mobile app.
The algorithms powering this product recommend a rich set of Gartner’s offerings like research documents, Peer Connect conversations, Events and Webinars in a highly personalized format to our clients that translated into an approximate dollar amount of 1.25 Million USD annually.
Developed predictive algorithms for estimating customer risk and churn.
Stipulated the workflows for setting up data pipelines for the Machine Learning models in production.
Developed NLP techniques to extract Key Questions, Topics from call transcripts and recommend research documents and SME’s to better prepare the sales team.
Increased customer retention from 42%to 39% which translates to about 1.5 Million USD per annum.
Developed hiring roadmaps and staffing requirements for projected product development plans.
Coached and mentored new Data Scientists, Data Engineers and Software Engineers in the team.

Publicis Sapient is a global consulting company that helps businesses drive digital transformation through strategy, design, and engineering. With a focus on delivering innovative and customer-centric solutions, Publicis Sapient serves a diverse range of industries, including financial services, healthcare, and retail. Its services include digital strategy, experience design, software engineering, and data analytics, among others. Publicis Sapient is part of the Publicis Groupe, one of the world's largest advertising and communications groups.

Senior Associate - Data Science

Nov 2017 - April 2019

Client : Mercedes Benz (DAIMLER) – OneDNA Platform

Lead the team of 5 Junior Data Science Engineer to build end to end Machine Learning platform to get the insight of customer behavior which translated into increase of customer retention by 72%.
Built Machine Learning & Deep Learning models using 100+ GB’s of Adobe clickstream data.
Created spark-based workflows for continuous ingestion of Data and perform Data processing and feature Engineering.
Scaling the solutions for all 35 markets (different geos) using distributed environment.
Integrating Model Output with Tableau dashboard which is primarily reviewed by CxO Suite of Mercedes Benz on regular basis.
Deployed the model on Azure environment using Azure Data Factory and Databricks which runs in batch and on Ad-hoc basis.
Demonstrating insights, models, mathematical and statistical concepts to non-technical stakeholders which helps them to plan the business strategies.
Increased the Test Drive booking by 40% by addressing the key pain areas faced by the customer while booking test drive which translated to increase in sales by 12%, increasing the overall revenue of the organization by 5%.

Client: Michael Kors

Building Recommendation Engine based on the Item-Item similarity to recommend items based on the customer journey and item selected.
Predicting the customers who are likely to be converted using Random Forest and XGBoost and assign them buckets for sales team to run campaign using email.
Customer retention and conversion increased by 60% translated into Increased the sales by 1.2 Million USD per annum by better recommendation.
Preparing data flow pipeline from FTP server using python and AWS.

Infosys is a multinational IT consulting and services company headquartered in Bangalore, India. Founded in 1981, Infosys provides a range of services including software development, maintenance, and engineering as well as consulting and digital transformation services and has a global footprint spanning over 50 countries.

Sr. Software Engineer - ML

Sept 2013 - Nov 2017

Client: American Express

Built PySpark based pipeline for data processing and wrote Hive Queries to fetch the data from CornerStone.
Built K-Means clustering algorithms to detect and avoid fraud transactions which translated to decrease in fraud transaction by 20%.
Developed the Python code in Spark environment to generate the insights for the customers to help them make business decisions.
Experienced with the Spark improving the performance and optimization of the algorithms in Hadoop using Spark Context, Spark-SQL, Data Frame, and Pair RDD's.
Responsible for design & development of Spark SQL Scripts using Python.

Freelance Projects

Data Scientist

Worked with a Startup which primarily works in Computer Vision and NLP Field as a freelancer.

Built a product from scratch to :
- Classify the documents in PDF or JPG format to different classes (i.e. Invoice, Resume).
- Extract NER from the documents.

Technology used: PyTorch, Flask, AWS,CNN, Neural Networks, Open CV