B4 Bachelor Recruiter & Employee Match  

• Benchmarking & Market Research

Compared thoroughly the existing hiring platforms on the market and found out the common issues that users faced while using these platforms.

Created surveys with Google Forms to research user experience with different hiring platforms.

• User Research Surveys

Conducted surveys to assess the important aspects of the employment process and to figure out the right questions to ask in order to find the top match.

• Research on Information Retrieval Systems

Compared ElasticSearch, machine learning tools and other information retrieval systems to decide which tool will help us best for our match-making algorithm.

• Web Scraping Data with Help of Python

Practiced web scraping from the Stackoverflow jobs & career page to have sample data for job offers and these data are used later on for testing.

• Cleaning the Data & Keywords Extraction

Used rake-nltk algorithm for keyword extraction to retrieve the first phrases for our sample data. Our GitHub repository here.

• Selecting the best feature ideas

Analyzed the results from our surveys and brainstorming sessions to come up with a list of ideas for our MVP.

• Designing application architecture

Defined the components and their connections for our web application using the Model View Controller design pattern.

Used Figma to to create the mockup design for our frontend.

• Set up the website backend

with Node.js, Express & MongoDB and established the connection between the database and ElasticSearch

• Implemented key features:

• Enhancements

Improved frontend by using Bootstrap and custom styling and restructured and refactored the backend.

• Deploying our web application

Researched on cloud platforms to decide on which one we use to deploy our first release and deployed our web application on Heroku.

Connected MongoDB Atlas and Bonsai Elasticsearch add-ons to our application on production.

• Improved usability based on user testing