AMMInnovation's Recruiting Made Simple

Revolutionize the recruitment industry, with efficient machine learning powered code and an intuitively minimalist user interface.

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Inspiration: Current recruitment practices are often unfair and biased, frequently causing qualified workers to lose opportunities. Both employers and potential employees suffer from this system, and bettering it would bring more jobs to qualified individuals, creating a better hiring environment for everyone.

What is Recruiting Made Simple?: A smart, and innovative solution to the current recruitment industry. It seamlessly combines 3 different languages and frameworks, to bring a quality user experience, detailed data analysis, and a constantly adaptable design.

How does it work?: The employer uses our XAML minimalist style UI to create a small set a "guiding values" for potential recruits, as well as set certain setting for their company. Those "guiding values" act as a baseline weight for the neural network. As the company hires and rejects applications, that data will be used to create a optimal multidimensional regression line. This line is constantly being updated as new data gets added, so when the company hires more people, qualified candidates will be automatically found. When a potential hire applies, an email listing all the details of the applicant as well as the cover letter(s) and resume is automatically sent.

Challenges along the way: Between learning WPF, and Python scripting, our team had various roadblocks on our road to success. For Michael, getting all the data from the WPF app-form parsed and put into a readable text format proved to be a challenging task. For Matthew, using Python to send images via email was a hurdle. For Alex, finding the perfect conversion formula from GPA and work experience to graphical weights took almost half the time, and another oddly large portion of time went into making text vertical.

Takeaways: Our diverse team picked up many practical skills throughout the course of the hackathon. Michael and Matthew both learned and applied languages they had never used the past, and Alex created a foundation for future machine learning development.

The Future: We plan to develop a much more expansive user interface, creating a much more detailed base weight space. The neural network itself can be improved on, adding more layers for more complexity and continuity between different data points and types.

We hope you enjoy our project!

  • Sincerely,

A M M Innovation

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