Job-a-cular

Title Page
My Bio
Content/ Index
What is Job-a-cular
Scenario 1
Scenario 2
How my app is related to these real world scenario?
How the application works?
Flowchart and Algorithm of Job Application screening over app
Applicant Profile screening Algorithm for Job-a-cular:
Prototype/ Wireframing of App (Using Figma)
Prototype/ Wireframing of App (Using Figma)
What are the Application of Job-a-cular?
What are the limitations of the Job-a-cular?
Thank you!

Inspiration

Tinder disrupted the market of match-makers or matrimony service agencies. These agencies are responsible for onboarding the bride/ groom, searching for the right match, and making them talk to each other to check the correlation. If both sides say yes, it will work. The same happens with the hiring agencies, department of outsourcing, department of selection and recruitment, and mainly the HR department of the organization. Understand the company’s requirements, get the application/ applicants from the outside, and match with the requirement, if it’s a match, he will be selected. In this process what’s the hectic step? Resume screening and matching the profiles with the requirement of org/ responsibilities associated with the job role.

What it does

Job-a-cular, a tinder kind of app for making employers meet job seekers. Job searching is not easy; everyone knows how it works and what it needs to pursue. On another hand, it is the same with the employer hiring the right candidate for their organization or team to bring the best onboard. This is where many hiring managers, human resource planners and managers, and selection & recruitment teams fail. Skills are not the matter here, time. Everyone is running with time and we need some systematic device to do our work perfectly as we do. Tinder disrupted the market of match-makers or matrimony service agencies. Just like how Tinder solved the issue of verifying the compatibility and similarities between both parties, our Job-a-cular solves the problem of finding the similarities and bringing up the profiles which match the requirement of the employer/ organization.

How we built it

Figma (Prototype and Wireframing) Twilio

Challenges we ran into

Preparing relevant algorithms for the backend operations using the minimum requirement and maximum proficiency levels

Accomplishments that we're proud of

We are proud that we made the resume/ CV screening, analysis, and filtering task automated. HR’s work will be minimized and can be utilized productively on other tasks.