I Love data engineering machine learning

Software engineer and data enthusiast always seeking out wisdom and experience whenever possible.
Experienced with data analytics and machine learning tools like Spark, Scikit-Learn, Jupyter, Python, Superset. Development experiences include Javascript, React, Node, Django, and much more.

Checkout my projects and resume here. More on my github. Let me know if you have any ideas we can work on :D

Projects and Experiences

Tesla
Tesla

Used Spark, Python, Hadoop, Pandas, Hive, Superset, and Airflow to process big data, develop anaysis, calculate metrics and create dynamic dashboards for internal Tesla engineering, design, and testing teams.

McKinsey & Co.
McKinsey & Co.

Developing Resftul API endpoints using Node.js and Express.js for evaluation system to make evaluations accesible for consultants. Unit testing routes, controllers and middlewares using Mocha. Working in Agile environment using toold like Jira for ticket managment.

Johnson & Johnson
J&J

Developed restful backend API using Symfony 3 and PHP for a medical application making patient and doctor interactions easier. Created library to parse and convert health database API into usable PHP objects for the front-end. Unit Test every feature including routes, controller, services, repositories to increase code coverage. Worked in Agile environment using tools like Jira, Confluence, Apiary and GIT.

Engineer at RIT
Developer

Developed respondent-driven sampling application using JQuery, Node, Loopback and MongoDB. Built the entire stack and impleneted invitation capabilty to allow for a system that can keep track of users who have completed the survey, and automatically invite more users. Responsible for deploying and maintaining application of RHEL server, and setting up NGINX for a Reverse Proxy Server.

Movie Predictor App
Movie Predictor

Engineered system to predict a movie's rating based on attributes from metadata found online. Used Pandas to clean and remove unwanted attributes. Used Numpy for matrix math and calculations. Utilized MatPlotLib to visualize data and find correlations. Apply Sckit-Learn machine learning algorthims like Logistic Regression, SVM and Decision trees to classify data. Uses cross section validation to come up with training data and testing data to test accuracy.

InstaAnalytics App
InstaAnalytics

Developed a web application that will predict how many likes your next Instagram post will get within 24 hours for a hackathon. Uses Instagram API to gather metadata and Sckit-Learn. Created predictive models using machine learning algorithms like linear regression. Developed some backend coding to use Instagram API to get user metadata and feed picture into Clariai API to gain tags about the pictures.