Mradul J.

Mradul J.

Machine Learning & Deep Learning professional having AWS certification with 7+ years of experience

Bengaluru , India

Experience: 8 Years

Mradul

Bengaluru , India

Machine Learning & Deep Learning professional having AWS certification with 7+ years of experience

102857 USD / Year

  • Immediate: Available

8 Years

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About Me

Mradul is PG in Machine Learning & Deep Learning certified from IIT, Madras and working as Senior Data Scientist with Kmart which is Largest non grocery retailer in Australia and New Zealand with 7.5 + years of experience in Data science industry...

He has also worked with IBM (Retail domain), Genpact(Healthcare) and start up Culture Machine (Social Media) and have a vast experience in idealization, conceptualization and production deployment of various ML/NLP projects.

He has a strong experience in building end to end data pipeline and model
deployment on cloud using AWS services.

 Responsible for defining, developing and communicating the key metrics and 
 model results to business stakeholders and management teams.

He has also mentored 50+ professionals to become data scientist/data engineer.

Analytical Skill Sets includes :
Tools: Python, PySpark, R, SQL, PostgreSQL and Sisense 
Cloud: AWS/Docker-Containerization/ML Model deployment
Framework/Packages: Pytorch, Keras, opencv, Scikit-Learn, Pandas, NLTK, BeautifulSoup, Genism, Matplotlib
Deep Learning/ML/NLP: CNN, RNN, LSTM, BERT, LightGBM, RandomForest, Clustering, Regularizations

Certifications:
AWS Cloud Practitioner
Deep Learning, IIT Madras

Accolades:
National Winner, Analytics Case Study, Conducted by IIT Kanpur
National Runners Up, Analytics Case Study, Conducted by IIT Madras
National Runners Up, Analytics Case Study, Conducted by SCMHRD

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Portfolio Projects

Description

Predicted the demand (market size) for the new apparel launched in the market using Light GBM ensemble modelling in python and deployed in production using docker container.

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Description

Identifythe similar product from the existing product database and using their sales profile to attach with new product for DC to Store allocation

Similar product identified by context matching (word2vec) and image matching (cnn)

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Description

Fashion apparel images have classified into Upper wear, bottom wear, full wear clothes along with identification of shoes.

Convolution neural network is build on the top of VGG16 and used transfer learning concept to build the neural network model.

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Description

Identify and categories the aspects from the customer reviews posted on the social media sites along with the sentiment analysis.

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