When Online Dating Tech Met IT Job Search
Here's how startup Path.To plans to use online dating technology to match IT job seekers with the right position at the right company.
Path.To intends to prove it can.
Even in a tight job market, IT professionals often have strong feelings about where they want to work, what makes for an excellent cultural fit, and what technical skills set them apart from the competition. Path.To says its online job service can use those factors and more to connect IT job seekers with the right employer.
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Unlike traditional job boards like Monster.com, Path.To relies on a compatibility scoring tool to analyze the personal and professional characteristics of each applicant in order to connect them to the right job. Currently, Path.To caters exclusively to Silicon Valley companies and job hunters but plans to roll out in other markets, including Chicago and New York, throughout 2012.
Free for job seekers, Path.To heralds a fresh approach to matching talent with job opportunities using a ranking system designed exclusively for the interactive design, software engineering, and IT professions.
[ There are a lot of factors that go into landing the right job. Read 5 Tips: Get Strong IT Job References. ]
Here are 6 things about Path.To you won't find on any of today's standard job boards and how techies stand to benefit:
No anonymity. Forget about blindly auditioning for an unknown employer. "With Path.To, job seekers are always dealing directly with a potential employer rather than a third-party recruiter or an intermediate with a company," says Darren Bounds, founder of Path.To. "That's really important because if you're using a site like Monster.com, you often don't even know what company you're applying to."
Weighted references. You know that annoying software engineer on LinkedIn with dozens of recommendations--90% of which were written by close family members? Well, the good news is Path.To depends on a sophisticated algorithm to weigh job candidates' recommendations based on factors such as the actual source of the referral and that referral's own technical skills. "Unlike other services like LinkedIn, with Path.To all endorsements are not created equal," says Bounds.
Personal preferences. Are you far more interested in working for a startup than an established company? Is your closet full of Armani suits or do you live for casual Fridays? How badly do you need a benefits package? "These questions alone can really impact what positions Path.To will recommend to a job seeker," says Bounds. "If you're a new college grad and single, benefits may not be very important to you. You may want to work for a ground-floor, high-tech startup that may not be around in six months." For this reason, candidates can state their preferences regarding dress code, benefits, and culture. In turn, Path.To's recommended job opportunities are delivered in a ranked order based on a candidate's preferences.
Social status. Every job seeker's Path.To account immediately taps into a user's interests on Twitter and Facebook. "By connecting to a Facebook or Twitter account, Path.To basically tries to understand what conversations a job seeker is engaging in and how these interests relate to a particular job opportunity," says Bounds.
Online celebrity. Unlike retail workers and healthcare professionals, techies tend to be involved in "vibrant online communities," says Bounds. Because of this, Path.To tracks each job seeker's contributions and reputation on online professional communities like Behance, Dribble, Forrst, and Github. "We actually look to see how engaged you are, what types of technologies you're engaged in, what types of design work you're involved in, what people think about your contributions, and what your reputation is inside these communities," says Bounds. "We then factor these in on a very granular level when matching candidates with a specific position."
Perpetual learning. Unlike static job boards, Path.To's ranking system actually learns from a job seeker's input. For example, a software engineer can either "Like" or "Dislike" the job opportunities they're presented with. Over time, the system becomes trained to recognize those jobs most likely to appeal to a particular candidate and to "reveal the right ones and bury the ones you don't like. The system is always learning about you," says Bounds.
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