Berlin based Jobspotting launches personalised job matching

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Finding a job is never easy, especially while screening hundreds and hundreds of online job posts – often without finding the right position. So, Jobspotting came to the rescue – its recommendation engine will is launched and users will get matched with relevant opportunities found across the web based on their skills, work experience and location.

Jobspotting is a data-driven recommendation engine using the latest skills graph technology and semantics analysis. It was started in 2013 by Hessam Lavi, Jan Backes and Manuel Holtz , whose combined experience on the Google Search team amounts to over 20 years. Robin Haak, co-founder of Axel Springer Plug and Play Accelerator, joined as co-founder and COO in September. Eventually, Jobspotting received a total of €500,000 in financing and with this service is taking a step further.

Jobspotting’s recommendation engine works quite simply. Users indicate their skills, work experience, seniority level and location in their profile. Out of over 200,000 jobs aggregated from over 20 job boards including Monster, StepStone, and AngelList Jobspotting proposes relevant job opportunities.

The engine learns from users interactions and feedback. Thus, users only get vacancies that actually are relevant to them – a completely new approach compared to the traditional job search. The service will start in Germany and the UK for jobs in IT and Marketing with expansion into new job verticals and countries planned in the coming quarters. In addition to the job matching, Jobspotting offers a magazine and company profiles. Users have access to the free service via the website or an iPhone app.

Ex-Googler Hessam Lavi, founder and CEO of Jobspotting, got the idea from his own job hunting experience: “When I first moved to Berlin from Dublin it was really hard to figure out where to start. Job sites are somewhat stuck in the ‘90s in terms of usability and relevance. I was bombarded with emails about mostly irrelevant vacancies, and it was a fulltime job to filter out the noise. I was fascinated by the simplicity of recommendations engines such as Spotify Radio, Pandora, and Netflix and it just made sense that there should be something similar for jobs.”