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- 1. Ritesh Kasat Email: riteshkasat@gmail.com Phone: (213) 675-3081 Website: riteshkasat.github.io Links: github.com/riteshkasat | linkedin.com/in/riteshkasat | stackoverflow.com/users/2355376/riteshkasat Objective Seeking a Full-time opportunity in the field of Software and Data Engineering starting May 2017. Education May 2017 MS, Computer Science (Data Informatics) University of Southern California, Los Angeles GPA: 3.6 Sep 2014 B.E., Information Science P.E.S. Institute of Technology, Bangalore, India GPA: 3.9 Technical Skills Programming Languages: Java, Python, C/C++ Web Technology: HTML, CSS, JavaScript, XML, JSON, AJAX, Bootstrap Databases: Mongo DB, MySQL, Oracle, PostgreSQL Data Science: Hadoop, Map Reduce, Scikit-learn, Data Mining, Machine Learning IDE: Eclipse, IntelliJ Idea, PyCharm, WebStorm Others: Git, Maven, Gradle, JUnit, Spring, Jenkins, Mockito, Agile, Apache/Tomcat Work Experience Jan 17 - Current Software Co-Op, Trifacta Inc. San Francisco Developing Python based Web Crawler to crawl all the webpages in Trifacta Wrangler and take screenshot of each visited page. Developed Mocha based page test cases for Trifacta Wrangler. Tech stack Python, JavaScript, Jenkins, Slack, Vagrant, Git, Phabricator, Mocha. Aug 16 - December 16 Student Developer, Perfit Los Angeles Developed Python based RESTful web-service for Perfit -a 3D modeling software- that auto- matically drapes clothes on the pre-constructed 3D model of a specific person. Implemented login and signup API for the website. Integrated google recaptcha authentication for login. Also responsible for setting up build automation and modularizing codebase using MVC design pattern. The web-service is hosted on AWS Elastic Beanstalk environment. Tech stack Python, Flask, RESTful web-service, PyBuilder, PyCharm, MVC, AWS Elastic Beanstalk. May 16 - Aug 16 Summer Intern, Trifacta Inc. San Francisco Developed a tool for customer success team that allows automatic import of scripts in the Trifacta Wrangler. Also developed several JavaScript automations such as automation to verify HDFS file permissions. Enhanced existing framework by creating new page models and refactoring existing ones. Tech stack JavaScript, Postgres, Jenkins, Slack, Vagrant, Sahi, Git, Phabricator. Aug 14 - Aug 15 Software Engineer, Intuit Inc. Bangalore Worked on the core development of QuickBooks Online, an accounting software used by millions all over the world. I worked with QuickBooks platform team to develop RESTful web-service in Java for subscription billing service. I also enhanced a JavaScript widget for UK Direct Debit payment facility. Test-driven development (TDD) was followed and I achieved an overall code-coverage of 90% for the new modules Tech stack Java, JavaScript, MySQL, Netezza, RESTful web-service, Jenkins, IntelliJ Idea, JUnit. Jan 14 - June 14 Software Co-Op, Intuit Inc. Bangalore Worked with the payments-platform team within Small Business Group of Intuit. During my co-op, I developed an algorithm in Java for automatic reconciliation of transaction in QuickBooks. This algorithm reduces 25 hours of manual effort each month to just 30 minutes. I was also responsible for designing exception handling strategy for the platform reconciliation service. All the code was reviewed, perfected and pushed to the production. Tech stack Java, JavaScript, Perforce, Git, Jenkins, Eclipse, Gradle, Maven.
- 2. Academic Projects Email Spam Classifier | github.com/riteshkasat/Spam-Classifier | Python, ML, Naive Bayes This is a Machine Learning Project that distinguishes spams from genuine emails. The model is learnt using Naive Bayes algorithm. This project also implements an enhancement called add-one smoothing. The performance of the classifier is determined using metrics such as precision, recall and F-1 score. I achieved an overall F-1 score of 0.98 for spam and 0.96 for ham. Large Scale Text classification of data | Python, Machine Learning, KNN This project aims at classifying a huge number of web documents into a hierarchy of categories. The large scale of data called for the use of advanced classification algorithms that could scale. Parallelized k-NN algorithm with the help of Hadoop Map-Reduce paradigm was implemented to handle space and time complexities of large data. Cosine-vector similarity was used to compare distances between two points instead of the euclidean distance with absolute distance in the traditional algorithm. Walmart Trip Type Classification | kaggle.com/rkasat | Python, Sklearn, Kaggle In this project, I worked on a Kaggle challenge by Walmart and secured overall 503rd rank in this competition. The problem here is to improve the science behind trip type classification, which will help Walmart to refine their segmen- tation process. I converted data to libsvm format, built models using classification algorithms such as Naive Bayes, K means, Random Forest and SVM, compared them to find accuracy, precision and recall. Movie Recommender System | github.com/riteshkasat/Item-Recommender-System | Data Mining In this project, I developed a user-based collaborative filtering recommender system for predicting movie ratings by a user. The ratings of the new movie for the given user were predicted using the similarity of its k nearest neighbors determined through their Pearson correlation coefficient. Coursework Software Engineering Machine Learning Data Mining Data Management Artificial Intelligence Algorithm Design and Analysis Web Technology Data Structures Compiler Design Operating Systems Computer Networks Computer Networks and Security Natural Language Processing OOP with Java Hackathons October 16 Money2020 Hackathon, The Venetian, Las Vegas Developed an app to order food using Amazons intelligent personal assistant Alexa. Using voice interaction, the user can order food under 1 minute. The payment for the ordered items is done using Visa Checkout API. A confirmation is also sent to the user using Twilio API. Tech stack Amazon Echo, JavaScript, MySQL, Twilio API, VISA API April 15 Data Science Hackathon, USC, Los Angeles Developed an app that could automatically detect interesting topics from the tweets. Topic detection was done using Latent Dirichilet Allocation - an unsupervised Learning algorithm. The topics were visualized using a python package called pyLDAvis. Tech stack Python, Machine Learning, Clustering Achievements Awarded with the title of Amul Vidya Shree for outstanding academic performance in CBSE class 10th Board examinations and securing first rank in the Guna District of Madhya Pradesh. Awarded with scholarship and certificate of First Class with Distinction for all the semesters in the undergrad course. Awarded with silver medal in National Mathematics Olympiad for securing 96 percent marks.