About Me
I am a Senior Data Scientist at Ciena. I am a graduate from IIT Bombay and an ex-employee of JP Morgan Chase & Co.
We live in a digital world today where humongous amount of data is generated on a daily ba...
We live in a digital world today where humongous amount of data is generated on a daily basis. The sheer capability of Data Science to extract tremendous business value from it, is what excites me. Along with software development, my core skills include overcoming the challenges of building a technical pipeline for data ingestion, pre-processing, model building, performance measurement and iteratively improving it. I look forward to ethically use the data and derive business value out of it.
I am a Senior Data Scientist with machine learning skills vetted by winning global AI competitions such as AWS DeepRacer. I am an avid self-learner and always enthusiastic about learning new skills. Ability to persevere, innovate and deliver timely results are my key strengths.
A few of my recent Machine Learning & Deep Learning achievements:
- I have used Reinforcement learning for building an autonomous driving vehicle in AWS DeepRacer. Won 4 separate events at Mumbai, national & global level. Represented JPMC and secured first place among 64 teams.
- I have used various ML and DL algorithms and built an end-to-end pipeline for Twitter Tweet Sentiment analysis in ML Mayhem competition. Won the competition among 20 teams for best ML pipeline.
- I have apprehended the capabilities, challenges, & consequences of deep learning via successfully completing a five course Deep Learning Specialization by deeplearning.ai. I have tested my knowledge via hands-on applications.
Skills
Others
Software Engineering
Web Development
Software Testing
Programming Language
Data & Analytics
Development Tools
Database
Hardware
Positions
Portfolio Projects

DeepRacer - Built Autonomous Driving Vehicle using Reinforcement Learning
https://www.youtube.com/watch?v=vCt-F2HscOUCompany
DeepRacer - Built Autonomous Driving Vehicle using Reinforcement Learning
Description
Self Driving Car Development | Won the global Financial Services Event for fastest self-driving car
•Used AWS SageMaker for training model used by car for completing the race lap autonomously
•Used EDA techniques for log analysis of training data to improve model & tune hyperparameters
•Overcame the challenge of reward function sniffing out an unforeseen behavior and improving it
Skills
Amazon SageMaker Deep Learning EDACompany
ML Mayhem - Machine Learning & Deep Learning
Description
Twitter tweet Sentiment Analysis | Won the competition among 20 teams for best ML pipeline
• Used advanced NLP techniques for data preprocessing & various algorithms for model training
• Built end to end pipeline for preprocessing, model training and performance measurement
• Extracted entities towards which sentiments were directed at using KNN, RNNs, LSTMs for NER
Company
Deep Learning Specialization
Description
Foundational Program of 5 courses: NN in TensorFlow | Understood the capabilities, challenges, & consequences of deep learning
• Neural Networks & Deep Learning: Built and trained deep neural networks, implemented vectorized neural networks, identified architecture parameters, and applied Deep Learning to various applications
• Improving Deep Neural Networks: Hyperparameter Tuning, Regularization & Optimization: Used best practices to train & develop test sets, analyzed bias/variance for building DL applications and applied optimization algorithms
• Structuring Machine Learning Projects: Used strategies for reducing errors in Machine Learning systems, understood complex Machine Learning settings, and applied end-to-end, transfer and multi-task learning
• Convolutional Neural Networks: Built a Convolutional Neural Network, applied it to visual detection and recognition tasks, used neural style transfer to generate art, and applied these algorithms to image, video, and other 2D/3D data
• Sequence Models: Built and trained Recurrent Neural Networks, its variants (GRUs, LSTMs), applied RNNs to characterlevel language modeling, used Word Embeddings, tokenizers and transformers to perform Named Entity Recognition
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