LinkedIn, the social networking company with one of the world’s first pioneering data science teams, has split its crew to be placed under different departments.
The data science team which had worked in the product division, had consisted of two branches through the years – the product data science team, responsible for “new data-powered features,” generating new data for analysis, and the decision sciences team, that tracks and monitors product metrics and usage data. VentureBeat reports that the former now comes under engineering while the latter answers to the office of the company’s chief financial officer.
Explains the head of LinkedIn’s business operations and analytics team and leader of the former decision sciences team, Laura Dholakia, while speaking to VentureBeat, “It was just really clear that there was just a lack of clarity around rules and responsibilities, which was frustrating for people on the team, as well as people who had to work with them.”
The reshuffle that took place about five months back, reorganised the company’s 150 data scientists with the choice to join whichever team they preferred. And it may seem that there have been positive turn-outs after.
“If anything, the reorg pairs up the analytics people, who focus on paid products like recruiting tools, with the data scientists, who look into the ways people use LinkedIn’s free “consumer” service for connecting with others. As for the product data scientists, working with the engineering staff reduces the potential for redundancy,” reports VB.
However not all have supported this move. Many key data scientists have left the organisation owing to the reshuffle.
VB sources believe that the reorganisation has sped up “decision making”.
“Suddenly you realize another part of the organization has a similar need, but that need is targeted on the product side,” Lutz Finger, director of data science and data engineering at the company tells VentureBeat in an interview. “What we did is actually we put both together. What has changed is I’ve tripled the number of algorithms I actually can test on my data, because I integrate the algorithms from the other team, which is pretty significant.”
A few months is not enough time to gauge how this move might affect the company at large. How the advantages add up remain to be seen.
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