1.Worked with a team of 4 members to Design,develop and deploy the automation tool (Swagger UI) and Integration of this tool with SNOW tool of world's largest multinational brewing industry.2.This automation is done by using python description rule based models followed by ML Classification models which includes Logistic regression, Random forest (both Grid search CV and randomized search CV),Naive Bayes as well as Xgboost Model.Increased the closure of change management assessment tickets (MTTA)of service now from 2 days to 4 minutes thereby increasing the efficiency of bot as well as ML model to 85%.3.Dedicatedly handled the support and enhancement phase of this project along with retraining of ML models using historical CR data.4.The above was done for EUR,GHQ,APAC(India) as well as MAZ zone.5.Actively involved with TECHCOE team as well as leading a team of 3 members in building scalable POCs with various clients.

1.Generating reports weekly and Monthly, fetching data from the Netezza Server to meet the daily requirements of Clients, Survey analysis, Post campaign conversion analysis and generating fortnightly reports and presenting to the stakeholders.2.Actively involved with Presales team to build scalable POC's .Build Vehicle Route optimization solution from scratch with end to end implementation of all the required constraints. Worked with fronted developer team to integrate the solution .Tools used-Python, GCP(Google maps Api key),Docker and streamlit.3.Worked with the team in building MMX models for leading automobile clients.Performing Exploratory data analysis,data cleaning and gathering data from various competitors.4.Developing contribution charts for the features that were finally used in linear regression model.

1.scrapping the images of various categories of proucts (using selenium web driver)from the Ecom-portal of client and de-duplicating the images using difpy library, image validation and building CNN,autoencoder and Isolation forest model for images and training the images with these pretrained models.2.Fine tuning the model with it's hyperparameters and bringing theaccuracy of the model close to 85 percent where category mismatch and intentional hiding of products can easily figured out.

1.Worked as a team to develop,build and deploy legal documents curation using NLP libraries such as Spacy,NLTK and gensim to custom summarize with an accuracy of 85 % for the legal documents (contract between two organization for MOU).2.Identifying top 15 key words from the documents using advanced NLP libraries.3.Deploying the solution in AWS EC3 instance.4.Actively involved in building various scalable POCs with in house presales team and proposing solutions involving recommendation systems to leading online restaurants delivery partners such as zomato.5.Building interactive dashboards using Tableau desktop for an HR analytics solutions and leading the team of 5 interns to deliver the solution.