Building Data Science Teams

http://radar.oreilly.com/2011/09/building-data-science-teams.html


Everyone wants to build a data-driven organization. It’s a popular phrase and there are plenty of books, journals, and technical blogs on the topic. But what does it really mean to be “data driven”? My definition is:

A data-driven organization acquires, processes, and leverages data in a timely fashion to create efficiencies, iterate on and develop new products, and navigate the competitive landscape.


I’ve found that the strongest data-driven organizations all live by the motto “if you can’t measure it, you can’t fix it” (a motto I learned from one of the best operations people I’ve worked with). This mindset gives you a fantastic ability to deliver value to your company by:


The Roles of a Data Scientist


Organizational and reporting alignment

As vague as that answer is, here are the three lessons I’ve learned:


What Makes a Data Scientist?

The term that seemed to fit best was data scientist: those who use both data and science to create something new.

But how do you find data scientists? Whenever someone asks that question, I refer them back to a more fundamental question: what makes a good data scientist? Here is what I look for:

These are some examples of training that hone the skills a data scientist needs to be successful:

And experiences like my own suggest that the best way to become a data scientist isn’t to be trained as a data scientist, but to do serious, data-intensive work in some other discipline.

Hiring data scientists was such a challenge at every place I’ve worked that I’ve adopted two models for building and training new hires. First, hire people with diverse backgrounds who have histories of playing with data to create some- thing novel. Second, take incredibly bright and creative people right out of college and put them through a very robust internship program.


=Hiring and talent= (内容非常好,而且和构建数据科学团队关系其实不大,比较有普遍意义)

Many people focus on hiring great data scientists, but they leave out the need for continued intellectual and career growth. These key aspects of growth are what I call talent growth.

Would we be willing to do a startup with you? (你是否合适加入) This is the first question we ask ourselves as a team when we meet to evaluate a candidate. It sums up a number of key criteria:

Can you “knock the socks off” of the company in 90 days? (俗话说的试用期?)

In four to six years, will you be doing something amazing? (长期规划)


Building the LinkedIn Data Science Team

What I found really surprised me. The companies all had fantastic sets of employees who could be considered “data scientists.” However, they were uniformly discouraged. They did first-rate work that they considered critical, but that had very little impact on the or- ganization. They’d finish some analysis or come up with some ideas, and the product managers would say “that’s nice, but it’s not on our roadmap.” As a result, the data scientists developing these ideas were frustrated, and their or- ganizations had trouble capitalizing on what they were capable of doing.

It’s important that our data team wasn’t comprised solely of mathematicians and other “data people.” It’s a fully integrated product group that includes people working in design, web development, engineering, product marketing, and operations. The silos that have traditionally separated data people from engineering, from design, and from marketing, don’t work when you’re building data products.

Interaction between the data science teams and the rest of corporate culture is another key factor.


But it’s a mistake to treat data science teams like any old product group. (It is probably a mistake to treat any old product group like any old product group, but that’s another issue.) To build teams that create great data products, you have to find people with the skills and the curiosity to ask the big questions. You have build cross-disciplinary groups with people who are comfortable creating together, who trust each other, and who are willing to help each other be amazing. It’s not easy, but if it were easy, it wouldn’t be as much fun.

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