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When it comes to big data insights, how do you know you’re asking the right questions? Hiring data scientists is a good start – we’re seeing their growth both on LinkedIn and at LinkedIn. But even data scientists are not immune from the myriad of hidden pitfalls that keep your key insights out of sight.
Drawing from a deceptively simple exercise that I’ve used to haze dozens of data scientists on their first day, I will discuss the good, the bad and the ugly lessons we’ve learned about asking the right questions, denominators and being a data skeptic.
Monica is a data scientist with a passion for turning data into products, actionable insights, and meaningful stories. As the VP of Data for Jawbone, she focuses on developing data-driven products that promote a healthier lifestyle and on finding stories in the UP wristband data.
Prior to Jawbone, Monica was one of the early members of the LinkedIn data science team, where she developed and improved some of LinkedIn’s key data products for matching jobs to passive candidates, discovering people you may know, and recommending groups you may like.
Monica’s compelling data stories are often picked up by the mainstream press, including the Wall Street Journal, The Economist, NPR and CNN. Monica holds a Ph.D. in Computer Science from CMU, where she focused on text mining and applied machine learning. She authored eight US patents and numerous papers that appeared in top-tier peer-reviewed journals and conference proceedings.
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