NLP-based job recommendation start-up, Roletroll.com, launches algorithmic job-search service in largest US tech and finance markets
June 17, 2014 (Newswire) - Former Goldman Sachs Vice President Adam Grealish launches Roletroll.com, a job recommendation engine for finance and tech jobs. The site uses unstructured data and statistics, collectively known as big data, to match users with jobs based on their unique skills and experiences.
Roletroll is a Brooklyn, NY-based start-up serving New York, Chicago and San Francisco.
Job seekers upload their resumes, and computer programs do the job-search grunt work for them, aggregating jobs from multiple sources and identifying jobs that are a particularly good fit. Already Roletroll has scored over 5 million individual job matches.
Roletroll uses technology, previously applied in quantitative finance to identify profitable trades, to identify profitable jobs. The site's proprietary matching technology draws from the fields of optimization theory, natural language processing and machine learning to identify and score matches.
The emergence of big data as a job-search tool came into sharp focus after the recent $120mm LinkedIn acquisition of Bright.com, which also went beyond traditional boolean search to find people jobs.
Unlike other job sites, Roletroll learns as users interact with jobs. Similar to Pandora, Roletroll tailors its recommendations as job seekers indicate interest in certain jobs. When you like or star a job, Roletroll determines what makes that job a particularly good fit and finds more like it.
Roletroll's specific focus on software development and finance allow for a deep dive into the nuances of tech and finance careers, picking up where larger aggregators leave off. The site sources jobs from specialized job boards and niche sites as well as those more frequently trafficked.
Centralized job search and tailored matches give users a personalized and targeted job-search experience. This comes at a time when hiring has picked up and more workers are entering the marketplace. Roletroll's user base includes active job seekers, in addition to those already employed who lack the time to perform a full search but are interested in hearing about a "perfect fit."
Scott Dobroski, career trend analyst at Glassdoor, has seen increases in job-seeker confidence.
"People are looking for a better fit, greater satisfaction and in some cases, a bigger paycheck," he said. "And now more than ever, people are doing a lot research about new potential opportunities and in the age of the Internet, you can get an exponentially better sense of what that job may actually entail."
Roletroll's first success story came from the founder. Mr. Grealish was engaged in a job search in early 2010. After growing frustrated with the tools available, he started coding.
The algorithms he built identified numerous jobs worth pursuing, some of which led to offers. Mr. Grealish was able to leverage those offers in the notoriously difficult Goldman Sachs interview process to secure a place on the FICC trading floor in New York.
"I was frustrated by how impersonal and unfocused jobs search was," said founder Adam Grealish. "Job seekers need full coverage of jobs in their space with results tailored to their specific skills. Roletroll fills that gap. It's Kayak meets OKCupid for jobs."
With its launch, Roletroll makes this matching technology available to all tech and finance job seekers.
Roletroll is a job recommendation engine for the tech and finance communities that uses big data to match people with jobs. Roletroll uses natural language processing and extensive machine learning to adapt to users preferences and provide hyper-personalized job matches.
Before founding Roletroll, Adam Grealish was Vice President at Goldman Sachs where he traded macro credit and structured products. Prior to that, he worked as a 'quant' at New York Life Investment Management. Adam graduated with Honors from the University of Chicago where he studied Economics and worked as Research Assistant to Nobel-laureate economist James Heckman.