Slideshow: Data Scientists Revisited


Every day we hear about the adventures of people who love data and know what to do with it. We again offer a random selection of data scientists -- and again we find a wide variety of expertise and experiences shaping the men and women of this new profession, data science.

Start the slideshow by clicking on the image below. And once you're done, feel free to drop a note on the message board regarding data scientists you know (or know about, at least), and take a look back at our first collection.

Monica Rogati
Current position: Senior research scientist, analytics, LinkedIn
Bio: Built the LinkedIn product analytics team from two to 10 data scientists. Spearheaded many of LinkedIn's key products: the Talent Match system that matches jobs to candidates, the first machine learning model for People You May Know, and the first version of Groups You May Like. She holds a PhD in computer science from Carnegie Mellon University. 
Quotable quote: 'By definition all scientists are data scientists. In my opinion, they are half hacker, half analyst, they use data to build products and find insights. It's Columbus meet Columbo --  starry eyed explorers and skeptical detectives.'
Current position: Senior research scientist, analytics, LinkedIn

Bio: Built the LinkedIn product analytics team from two to 10 data scientists. Spearheaded many of LinkedIn's key products: the Talent Match system that matches jobs to candidates, the first machine learning model for People You May Know, and the first version of Groups You May Like. She holds a PhD in computer science from Carnegie Mellon University.

Quotable quote: "By definition all scientists are data scientists. In my opinion, they are half hacker, half analyst, they use data to build products and find insights. It's Columbus meet Columbo -- starry eyed explorers and skeptical detectives."

Gil Press, Managing Partner, gPress

Gil Press is Managing Partner of gPress, a marketing, publishing, and research consultancy. Prior to establishing gPress, he held senior marketing positions with DEC and EMC. Most recently, he was Senior Director, Thought Leadership Marketing at EMC, responsible for online and print thought leadership communications and a number of high-impact global marketing programs. He is on the Editorial Board of Ubiquity, an ACM publication, and blogs at http://whatsthebigdata.com and www.infostory.wordpress.com.

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Re: Good mix
  • 7/5/2012 11:42:46 AM
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Well, remember what some people define as the key to magnificent analytics:

The 90/10 rule -- spend 10% on technology and 90% on talent.

Who agrees?

Re: Good mix
  • 7/3/2012 9:32:55 PM
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I like the quotes about data not just being terabytes and the other quote that it's storytelling.  I have often seen this in a couple of work places where there was plenty of data, but not enough talent to make sense of it. 

Re: Good mix
  • 7/3/2012 2:09:13 PM
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Let's hope these tech students also nurture their creativity and curiousity.

Re: Good mix
  • 7/3/2012 12:28:01 PM
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I agree, Jen. I know University of Illinois received a record number of applicants for its undergrad Engineering School, a top-rated program, this year. I would suspect other schools, and other tech disciplines, would report the same. 

Re: Good mix
  • 7/3/2012 12:16:26 PM
NO RATINGS

I love the way young people are expanding their tech knowledge and going into the tech fields whether they are men or women.

Re: Good mix
  • 7/3/2012 10:45:17 AM
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Isn't that the truth Noreen! I keep telling my own soon-to-be college freshman, "Think data science." I'm hoping one day she'll realize I'm not totally bonkers. 

Re: Good mix
  • 7/3/2012 10:43:03 AM
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Really interesting post Gil. I'm sharing with all the college freshmen I know!

Good mix
  • 7/3/2012 9:45:30 AM
NO RATINGS

Hi Gil, interesting mix of people here (and, as a female, I'm pleased to see several women mixed up in this bunch of data scientists). I particularly like Monica Rogati's quote, "By definition all scientists are data scientists. In my opinion, they are half hacker, half analyst, they use data to build products and find insights. It's Columbus meet Columbo -- starry eyed explorers and skeptical detectives." That last piece brings to mind the "Holmes vs. Columbus" Point/Counterpoint debate we ran not too long ago!

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