Being brought up in a rural area of Haryana, computer was an alien thing for me back in 2008 when it was introduced in our school. Apart from using Microsoft Paint in school hours, I was keen on diving deeper into it.
Fast forward today, I pride myself as a software engineer who loves to build scalable system from scratch, uncover the hidden facts and meaningful insights from messy data, building and deploying secure APIs for consumers.
I, personally, love the idea of improving the world with a block of code.
New Delhi, IndiaSoftware Engineer II
• Designed and implemented scalable, fault-tolerant data pipelines on AWS that processed over 1 TB of data per day.
• Developed and deployed a suite of interactive dashboards using R Shiny that received over 10,000 monthly active users and resulted in a 25% increase in user engagement and a 37% decrease in support requests.
• Providing technical support to internal teams and external clients to leverage the full potential of AWS.
• Single-handedly created a prototype in React and Flask to enable internal users submit and manage batch jobs from a single platform.
Pune, IndiaSoftware Engineer
• Created and deployed demand forecasting machine learning models for retail energy provider to eliminate uncertainty with confidence and operate at their full potential.
• Conducted research on big data provided by MasterCard and came up with the probability to sell a product in a particular geo-area in United States. Built a back-end in Flask to support the analysis.
• Single-handedly built internal recruitment portal using GoogleAPIs and Flask framework.
• Developed RESTful APIs for a product definition app and performed routine data analysis for international clients.
Bengaluru, IndiaSoftware Engineer, Intern
• Implemented an end-to-end auto-suggestion tool to find the semantic
similarity between the solution of student’s queries and deployed the model
as a REST-API on AWS EC2 using Flask backend. The proposed system
drastically reduced the query resolution
time of 72% of students from 2 days to 13.3 minutes
Developed a dashboard to analyse the Key Performance Indicator (KPI) using SQL queries.
• Built an NLP tool to calculate the percentage of code and text in TA’s responses to increase the overall interaction of students and TAs.
• Wrote scripts to send emails and migrate 1500+ students from Flock to open-source platform Mattermost.
• Revamped InterviewBit’s webpages and worked on content creation of Data Science, Machine Learning and Deep Learning.
RemoteData Science, Intern
• Fostered 200+ students to improve their skills by providing clear, positive and line-by-line actionable feedback on their submitted projects using upGrad's code review tool for data science courses.
New Delhi, IndiaData Analyst, Intern
• Analyzed sentiments of public on different government policies and
campaigns on social media.
• Exported over 4000 tweets with Twitter API and built a hybrid solution to classify each tweet as positive or negative with KNN algorithm.
• Performed data mining operations on websites for various internal purposes.
LPU, IndiaLead Organizer
• MSTC host community events to guide professionals in different
technologies. We strive to create a platform where like-minded individuals
come together to share and learn about technology.
• As a lead speaker, I share my knowledge about front-end technologies.
International Journal of Emerging Technologies and Innovative Research (www.jetir.org), 5(12), 598-605
Think India Journal, 22(3), 8382-8391
ACM - International Conference Proceedings Series (Jun, 2019)
Problem Statement: Build an algorithm to
best identify potential donors.
• My goal was to evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.
• Implemented a pipeline in Python that will train and predict on the supervised learning algorithm given. I used the Grid Search method to tune the parameters of all algorithms and Gradient Boosting Classifier to extract features importance.
Problem Statement: Build an app to
prediction indian paper currency.
• Collected the images from search engines and trained a Convolutional Neural Network (CNN) model to predict the 7 types of Indian paper currency i.e. 10, 20, 50, 100, 200, 500, 2000.
• Deployed the machine learning model on Heroku with Flask back-end and secured the app with CSRF protection.
Problem Statement: Building a command-line
application to predict flower class along with the
• AI image classification and machine learning utilizing the PyTorch framework.
• Used transfer learning on pre-trained architectures including vgg11, vgg13, vgg16, vgg19, densenet121, densenet169, densenet161, and densenet201.
• Trained dynamic neural networks in Python with GPU acceleration with 85% accuracy.
Problem Statement: Customer Churn Prediction
from a Music App Spark
• Used PySpark to analyze the data of a fictional music app Sparkify to identify the factor affecting the customers who are most likely to churn.
• Trained machine learning model on IBM Cloud with the accuracy of 83.87%
Problem Statement: Analyzing the public data of StackOverflow Survey 2017
• Analyzed the StackOverflow public data of over 64,000
developers around the globe for the year 2017.
• Answered questions like, how education may influence the salary, gender ratio of developers across the world, the rate of increase in salary with years of experience and does more language implies more salary hike in IT sector.
Problem Statement: Identify Customer
Segments with Arvato Dataset
• The data and design for this project were provided by Arvato Financial Services. I applied unsupervised learning techniques on demographic and spending data for a sample of German households.
• Preprocessed the data, apply dimensionality reduction techniques, and implement clustering algorithms to segment customers with the goal of optimizing customer outreach for a mail order company.
• The objective was to find relationships between demographics features, organize the population into clusters, and see how prevalent customers are in each of the segments obtained.
Problem Statement: Building a machine
• Analyzed disaster data from Figure Eight to build a model for an API that classifies disaster messages.
• Created a machine learning pipeline to categorize the events so that it can send the messages to an appropriate disaster relief agency and deployed as a web app.
Problem Statement: Get real-time updates of
cricket matches on your desktop
• Get real-time push notifications on your desktop on every Four, Six and fall of a wicket of Indian Premier League's matches
• Used an HTTP persistent-connection to extract the live score from a webpage
Problem Statement: Get the name of the
continent in which a country is located in.
• Built a pretty simple Python package to understand how packages work on PyPi.
A Project made under Microsoft Technical Community LPU
• The project centers around developing an URL shortening
service. You have got long URLs that are hard to remember.
shortTo.com to shorten your URL and give them an easy to
Twitter, one of the largest social media site receives tweets in millions every day. This huge amount of raw data can be used for industrial or business purpose by organizing according to our requirement and processing. This project provides a way of sentiment analysis using hadoop which will process the huge amount of data on a hadoop cluster faster in real time
• Hacktoberfest is a month-long celebration of open source
in partnership with Github, in which participants need to
4 Pull Request across the Github.
• Hacktoberfest Status Checker is an open-source tool to know the status of your Hacktoberfest activities in the month of October.
Sales data was given in CSV format and the task of this project was to derive valuable insights from the raw data, like:
• Which product was most sold?
• What payment modes were used for purchasing products?
• What is the most common payment method for the United States?
• What was the earliest time of the day a transaction occurred?
• Were there repeat customers? Discuss possible issues.
• Smart Q Labs is a dynamic queue management solution which
care of your queue number and gives you notification time by
time. Not only that we provide analytics for the outlets so
they can manage as well as enjoy managing queue.
• I designed the mobile website using the concept of PWA (Progressive Web Apps), specially designed to work in the offline mode or bad network connection. PWA uses modern web capabilities to deliver an app-like experience to users. It uses the app-shell model to provide app-style navigation and interactions.
The dataset contains information of people die from "Diabetes Mellitus" between 1999-2015. The task of this project was to derive valuable insights from the raw data, like:
• In what state has the most deaths occurred?
• Over what period (start and end) was the data collected?
• What were the total number of deaths for 2006?
• What state had the least deaths in 2001?
• How many people die from "Diabetes mellitus" over the entire reporting period?