Facebook is holding a Kaggle competition to find new data scientists.
If you don’t know what that means, welcome to the club: You’re not a data scientist.
Kaggle is a web site for data scientists. It allows individuals or companies to dump their data onto the site, and then Kaggle’s 100,000 or so members create analytical and predictive models based on the data. It’s crowdsourced Big Data analysis, basically.
Facebook has several data scientist vacancies available right now, GigaOm noticed, and rather than recruit the old fashioned way — resumes, interviews and so on — the company is holding a contest. The winners, the people who provide the best set of predictions based on an analysis of a database of millions of text questions, will get jobs at Facebook.
This competition tests your text skills on a large dataset from the Stack Exchange sites. The task is to predict the tags (a.k.a. keywords, topics, summaries), given only the question text and its title. The dataset contains content from disparate stack exchange sites, containing a mix of both technical and non-technical questions.
Positions are available in Menlo Park, Seattle, New York City, and London; candidates must have, or be eligible to obtain, authorization to work in the US or UK.
Stack Exchange is a site on which tech professionals post questions and write answers. The Facebook Kaggle contest is basically asking candidates to sift Stack Exchange’s database of questions using a statistical method to predict which keyword tags a given question will fall under. (As a normal, I may have misunderstood this. Forgive me.)
Facebook has used Kaggle contests for job applicants before. So if you felt the Stack Exchange test was a bit too hard, maybe you could practice on this old Facebook Kaggle challenge from 2012:
The challenge is to recommend missing links in a social network. Participants will be presented with an external anonymized, directed social graph (no, not Facebook, keep guessing) from which some edges have been deleted, and asked to make ranked predictions for each user in the test set of which other users they would want to follow.
Easy-peasy-lemon-squeezy — if you’re an analytics nerd.
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