For one thing, he had given Goldman a way to circumvent the traditional product release cycle by publishing small modules in the form of ads on the site’s most popular pages. Luckily, Reid Hoffman, LinkedIn’s cofounder and CEO at the time (now its executive chairman), had faith in the power of analytics because of his experiences at PayPal, and he had granted Goldman a high degree of autonomy. Why would users need LinkedIn to figure out their networks for them? The site already had an address book importer that could pull in all a member’s connections. Some colleagues were openly dismissive of Goldman’s ideas. But LinkedIn’s engineering team, caught up in the challenges of scaling up the site, seemed uninterested. He could imagine that new features capitalizing on the heuristics he was developing might provide value to users. He began forming theories, testing hunches, and finding patterns that allowed him to predict whose networks a given profile would land in. It all made for messy data and unwieldy analysis, but as he began exploring people’s connections, he started to see possibilities. Goldman, a PhD in physics from Stanford, was intrigued by the linking he did see going on and by the richness of the user profiles. So you just stand in the corner sipping your drink-and you probably leave early.” As one LinkedIn manager put it, “It was like arriving at a conference reception and realizing you don’t know anyone. Something was apparently missing in the social experience. But users weren’t seeking out connections with the people who were already on the site at the rate executives had expected.
The company had just under 8 million accounts, and the number was growing quickly as existing members invited their friends and colleagues to join. When Jonathan Goldman arrived for work in June 2006 at LinkedIn, the business networking site, the place still felt like a start-up.
#GLASSDOOR VENTURE FORTHE HOW TO#
In this article, Harvard Business School’s Davenport and Greylock’s Patil take a deep dive on what organizations need to know about data scientists: where to look for them, how to attract and develop them, and how to spot a great one. Bringing those disparate worlds together, he crafted a solution that dramatically reduced fraud losses. One data scientist who was studying a fraud problem, for example, realized it was analogous to a type of DNA sequencing problem. And they don’t just deliver reports: They get at the questions at the heart of problems and devise creative approaches to them. They find the story buried in the data and communicate it. They bring structure to it, find compelling patterns in it, and advise executives on the implications for products, processes, and decisions. Indeed, Greylock Partners, the VC firm that backed Facebook and LinkedIn, is so worried about the shortage of data scientists that it has a recruiting team dedicated to channeling them to the businesses in its portfolio.ĭata scientists are the key to realizing the opportunities presented by big data. As companies wrestle with unprecedented volumes and types of information, demand for these experts has raced well ahead of supply. Today data scientists are the hires firms are competing to make.
Back in the 1990s, computer engineer and Wall Street “quant” were the hot occupations in business.
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