![]() ![]() It didn’t take long for LinkedIn’s top managers to recognize a good idea and make it a standard feature. The shortage of data scientists is becoming a serious constraint in some sectors. Goldman and his team also got the action required to respond to a suggestion down to one click. Goldman continued to refine how the suggestions were generated, incorporating networking ideas such as “triangle closing”-the notion that if you know Larry and Sue, there’s a good chance that Larry and Sue know each other. The click-through rate on those ads was the highest ever seen. Within days it was obvious that something remarkable was taking place. He did this by ginning up a custom ad that displayed the three best new matches for each user based on the background entered in his or her LinkedIn profile. Through one such module, Goldman started to test what would happen if you presented users with names of people they hadn’t yet connected with but seemed likely to know-for example, people who had shared their tenures at schools and workplaces. 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. 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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