Login screen: coming soon: aimbrain authentication
A "standard" Jobseeker's Profile
Example Student Organisation profile
Example Recruiter Profile
Interview platform, webex integration in progress
Example view of a jobseeker applying to many jobs through a campaign
Recruiter-jobseeker matching in action
Job matching algorithm (page 1)
Job matching algorithm (page 2)
A common theme among sponsors in the hackathon opening ceremony was their interest in hiring participants. We want to reduce the effort required by jobseekers, recruiters and event organisers in the recruitment or event organisation process, effectively bringing everyone closer together.
What it does
In essence, Hirelah is a webapp.
- It contains an events platform that allow student organisers/recruiters to create events, such an example would be ICHACK itself. We proposed, as an effort to counter bot sign ups and illegal ticket transfer, to use AimBrain’s authentication APIs to allow users to register using their biometric identity. Attendance in the event would simply be taken via biometrics. Due to time constraints, we weren’t able to get the API running.
- It has a campaign platform that allows student bodies to promote jobs for their partner/sponsor companies. Students/graduates are able to, through one campaign, apply to many partner companies. An example would be a DoCSoc ICHACK sponsors campaign, in which jobseekers will be able to apply to jobs posted by sponsors in one application.
- It has a jobs platform which allows recruiters to add jobs just like a normal jobs portal. Jobseekers are able to apply to jobs and track their progress. We aimed to get Cisco’s Webex Video API running to facilitate video interviews, but unfortunately the core components of the project took up most of the development time.
Job allocation algorithm
Hirelah also implements a job allocation algorithm that matches recruiters with jobseekers. This algorithm allows optimal matching between recruiters and jobseekers, given that companies have limits on the number of recruits in each job. This optimal matching also means that the yield rate will be higher.
The algorithm works as follows: after interviews, recruiters can rank applicants, and applicants can rank recruiters. Given these scores and constraints given (i.e. number of recruits), the algorithm optimizes and matches recruiters and applicants. A screenshot of the code in action is in the Image gallery; we did not have time to implement the front end of it.
How we built it
We used React, Redux, GraphQL, and Django. Our testing site is hosted on Firebase.
Challenges we ran into
- Underestimation of project complexity: we weren’t experienced React/GraphQL programmers, and a lot of time was spent on StackOverflow.
- Getting the APIs running; we were mostly preoccupied with getting the front and back end of the core components of the platform up, and unfortunately we weren’t able to, in the given time, get Cisco’s and AimBrain’s APIs up.
Accomplishments that we're proud of
Successfully implemented minimal core components that allow it to be a talent acquisition platform.
What we learned
Plan better and don't underestimate the effort required in a project.
What's next for Hirelah
- Successfully implement AimBrain and Cisco APIs
- Refactor Code
- Release the product