This document proposes an automated job search and application system using machine learning algorithms. It would search postings, identify good matches for a job seeker, automatically apply to those jobs on their behalf, and notify them. This could streamline the inefficient process of searching, applying, and filling out forms online. It aims to make the process more focused on the job seeker rather than employers. The proposal outlines forming a team, securing funding, developing the system over 2017, and marketing it to the millions of Americans who search for jobs online each year.

1. Automated, Aggregate, Machine
Learning Job Search
Project Proposal
Nicholas Wasko
Orlando, FL

2. Problem
• Looking online jobs is a primary method for low to medium skilled job
searchers
• Finding the appropriate posting and filling it out is time consuming
and inefficient
• The current market is very much centered around the employer, not
employee
• The process can be optimized using Machine Learning (ML) algorithm
• Deep search algorithms also help

3. Solution
• Software that
• searches and sifts through all online jobs using machine learning
• Not using solely KEYWORDS and an input
• Find best potential jobs
• Applies to jobs automatically
• Notifies customer of application, gives information

4. Where's the Money?
• Unemployed Job Seekers 7,000,000 per annum
• Many employed job seekers also looking
• More than half use online job applications
• Ratio of applicants to Jobs is around 100
Market this service to these people to make online job searching easier and more efficient
More data posted on Randrr website (a competitor that opened up in December 2016)
• http://www.bls.gov/cps/cpsaat34.htm
• http://www.pewinternet.org/2015/11/19/searching-for-work-in-the-digital-era/

5. Current Landscape
• Jobs are posted on variety of sites:
• Job search sites
• Company websites
• Social networking sites
• University websites
• Staffing firms
• HR Firms (ADP)
• Craigslist
CHAIOTIC for TODAYS JOB SEEKER

6. The New Landscape
• Job seeker fills out ONE, well detailed application
• Our software uses this information to find and apply to jobs, sell
resumes to staffing firms, recruiters
• Applicants are contacted directly via HR personelle
• Limited technical skills for the job application! Saved Time! More
comprehensive! Less personal?
• Like TURBOTax, or the COMMON APPLICATION for college

7. What We Need (Team Formation)
• Experienced CTO for mentorship
• High level, experienced programmer in ML, SEO
• Programmers with experience in autofill
• Mid level programmers
• Interns
• Physical resources: cloud storage, computers, programs, database, servers
• Use of consultants if necessary

8. What we need (Financials)
• Money for servers, hardware ($10,000)
• Marketing ($10,000)
• Labor (free to $20,000)
• Website and app platforms (5,000)
• Classified as: LOW RISK, HIGH RETURN?

9. Competition
• Randrr
• CareerShift
• Roboform
• SpringRole
• We differ in the algorithm,
marketing, brand, partnerships,
and incentives

10. Timeline
• Febuary 2017, team meetings
• March 2017, Begin development
• Marketing during development
• June 2017, Finish protype
• Bring to customers
• Continue marketing

11. Questions