The Sexiest Job of the 21st Century

這期的HBR很很不幸的又是圍繞big data打轉, 裡頭有一篇文章是這樣子的, “The Sexiest Job of the 21st Century”

Some notes left for myself, and to whom may want to become a good candidate in field of data analysis

文章的作者列出了十點有關於, “How to Find the Data Scientists You need”
At least, I should be heading to meet point 2, 3, 4, 7, 8, and 10. Here are they.

How to Find the Data Scientists You Need

1. Focus recruiting at the “usual suspect” universities (Stanford, MIT, Berkeley, Harvard, Carnegie Mellon) and also at a few others with proven strengths: North Carolina State, UC Santa Cruz, the University of Maryland, the University of Washington, and UT Austin.
2. Scan the membership rolls of user groups devoted to data science tools. The R User Groups (for an open-source statistical tool favored by data scientists) and Python Interest Groups (for PIGgies) are good places to start.
3. Search for data scientists on LinkedIn — they’re almost all on there, and you can see if they have the skills you want.
4. Hang out with data scientists at the Strata, Structure:Data, and Hadoop World conferences and similar gatherings (there is almost one a week now) or at informal data scientist “meet-ups” in the Bay Area; Boston; New York; Washington, DC; London; Singapore; and Sydney.
5. Make friends with a local venture capitalist, who is likely to have gotten a variety of big data proposals over the past year.
6. Host a competition on Kaggle or TopCoder, the analytics and coding competition sites. Follow up with the most-creative entrants.
7. Don’t bother with any candidate who can’t code. Coding skills don’t have to be at a world-class level but should be good enough to get by. Look for evidence, too, that candidates learn rapidly about new technologies and methods.
8. Make sure a candidate can find a story in a data set and provide a coherent narrative about a key data insight. Test whether he or she can communicate with numbers, visually and verbally.
9. Be wary of candidates who are too detached from the business world. When you ask how their work might apply to your management challenges, are they stuck for answers?
10. Ask candidates about their favorite analysis or insight and how they are keeping their skills sharp. Have they gotten a certificate in the advanced track of Stanford’s online Machine Learning course, contributed to open-source projects, or built an online repository of code to share (for example, on GitHub)?