Elaichi Co. Online retail for Home goods and decor. Cultural-inspired home decor infused with Hindi elements to enrich your space & teach you the language.I design, create, photograph and ship the products, along with managing the social medias and website. This passion project came about while learning Hindi. My journey to learn Hindi began with covering my home with flashcards and post-it notes, associating words with everyday objects. As I delved deeper into my language learning, I realized a gap in the market. There was a lack of everyday household items and decor with Hindi language integration.

- Managed, resolved, and triaged tickets reported by customers and customer support team- Worked with Engineering teams to create feature requests and resolve bugs found on platform- Respond to customer tickets giving resolutions and explaining issues they may have in non technical terms- Create documentation for new and existing features and integration requirements -Collaborated with Customer Support team and Backend Engineering team to build out features and fix bugs found in the platform-Presenting technical solutions to explain new features and releases to non technical customers and stakeholders -Creating Integrated standard procedures to transfer data into and from customer salesforce instances-Led Xplenty integration, maintenance and maintained communication with Xplenty to resolve bugs and add features-Trained new Product Support hires remotely on the fullcast Database, Xplenty, and QA processes

- Created complex MySQL queries to filter data and create metrics for customer sales teams- Integrated the Xplenty platform to import and export customer data from Amazon, Salesforce, SFTP file systems and fullcast platform- Communicated with the Customer Support team and backend engineering to fix bugs found in the platform- Worked directly with customers to build out metrics and tables on the fullcast platform

Using 2020 ranked games, Riot Games API and web scraping of na.op.gg, created a database of users and their stats to generate a user to user recommender system. This recommender recommends new champions for a user to play based on their data . Tested multiple recommenders evaluating based off of the class tag percentage (a class tag helps describe the champion). Compared their tag percentages of the original to recommended champions and used RMSE score to find the best one.

Used machine learning to created a model to predict if an applicant will become delinquent on a loan payment, given information provided in a typical loan application. The Data provided was from already approved loans, I created a model to be used as a second layer approval system to better predict if a loan applicant would become delinquent on their payment.

Web scraped data on cosmetic product ingredients and performed hypothesis testing (Welsh T-test) to compare the expensive with the inexpensive brands and the number of cancer causing chemicals in each.

Cashier/Usher

Cashier/Usher