Are You Recruiting A Data Scientist, Or Unicorn?
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User Rank: Ninja
11/27/2013 | 3:04:10 PM
Re: Spread the wealth
Laurianne, that is a very good question, large firms have the capacity to predict into the future however, I am not too sure in the ability of small and medium firms being able to invest in data scientist in the same way.

It makes me wonder, since out sourcing, cloud technologies, unemployment being at around 7% in the U.S at the moment, and per capita income being around 50k. It might be possible to put together an online team of many to do the job of one. If anyone has come across a startup like this, it would be interest to study it.
User Rank: Apprentice
11/27/2013 | 3:08:31 AM
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SQIAR ( is a leading global consultancy which provides innovative business intelligence services to small and medium size (SMEs) businesses. Our agile approach provides organizations with breakthrough insights and powerful data visualizations to rapidly analyse multiple aspects of their business in perspectives that matter most.
User Rank: Author
11/21/2013 | 2:05:35 PM
Re: Spread the wealth
Training up people to be analytics specialists and then embedding them inside business groups -- where they could combine the analytics knowledge with the business group knowledge, say marketing or manufacturing logistics -- sounds quite appealng. But are companies willing to invest in the training?
Ellis Booker
Ellis Booker,
User Rank: Strategist
11/21/2013 | 10:30:11 AM
Spread the wealth
Instead of thowing money at a small group of people (who'll be recruited like crazy even if you manage to snag them), companies should use these inflated budgets to expand the ranks of, let's call them, "data-competent" workers inside every part of the business. Employees aware of the basic ideas around big data and data analytics--and the data assets that exist inside the organization--will dream up all kinds of interesting uses, I bet. Put another way, the first step is coming up with an interesting problem or senario that'll be valuable to the business. 
User Rank: Apprentice
11/21/2013 | 9:15:48 AM
"Upadhyay offered this example: Say you hire a team of 10 data scientists with an average annual cost of $150,000 per employee. "That's $1.5 million for a data science team," he said. "So they better be creating at least $15 million dollars in value for you -- 10 times [the expense] -- to be worth it.""

I wonder how long that would be allowed to climb to that level. You're not going to get to 10x right away, but would a year of two of 2x or 3x be satisfactory in the build up, or would it be shut down first usually?

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