Building Data Science Teams

by D.J. Patil

As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success. Topics What it means to be "data driven." The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team.

Genres: Business, Management, Nonfiction, Technology, Science, Stem, Professional Development

34 pages, Kindle Edition

First published September 15, 2011

Reviews

[Prateek Jain · Rating 2 out of 5]

Building Data Science Teams (Kindle Edition) by DJ Patil gives information about (a) how the linkedin data science team was built (b) what are the different attributes to look for hiring people interested in joining your organization as data scientist.

There is nothing exciting or interesting in the book and it is very high level. It read more like Data Science is different, so lets hire folks with diverse backgrounds and run things differently.

[Rafael Ghossi · Rating 3 out of 5]

Leitura bem curta e sem muita profundidade, mas útil em algumas opiniões dadas pelo autor que pode se aplicar ao cenário da sua empresa.

[Srikar · Rating 3 out of 5]

Predates the current artificial intelligence machine learning hoopla. Mostly relevant nevertheless.

[meesh · Rating 3 out of 5]

Altogether, an informative read for help framing the data scientist role to newcomers, discussing their core competencies, along with some tips on how to hire for the role and build an effective team.

I felt some of the hiring tips around under the "Would we be willing to do a startup with you?" question section are ripe for projecting biases, so I would caution against blindly using that as a point of reference, and would have preferred the author to do the same.

Notably there's no reference to ethics either, though I suppose in 2011 the field was younger and at least the topic crops up more often in the author's later writing.

[Leonardo Longo · Rating 3 out of 5]

The book title promises an overview of skills, tools and perspectives behind data science groups, but the set of skills presented is a very superficial view of the topic, with room for exploring more how technical teams work together with businesses one.
I didn't had much expectations on going deep into the tools, as they get obsolete really fast and a Gartner report could give a more accurate overview of the actual landscape, but presenting it in such a brief chapter is much less than I've expected.
It's a good starting point on the subject, but needs to explore more at least the three topics presented on the book's title.

[Ray · Rating 3 out of 5]

What I enjoyed about this was its account of how LinkedIn built its data science team, and some of its philosophies on finding and nurturing talent. Overall, it was a good quick read that was relevant and helpful during the time I was first being introduced to data science as a discipline and career.

[Ed Barton · Rating 4 out of 5]

Quick Monograph On Data Science

A decent read on the approach used by the author in building out the data science team at LinkedIn. Focusing on the role of the team and some of the traits to look for in the hiring process, there is enough here to point you in the right direction as you build out a data science function. Decent read.

[Daron Yondem · Rating 4 out of 5]

Definitely a very short read. It gives a very high level look of building a data science team.

[Ashish Om Gourav · Rating 5 out of 5]

This is an excellent & concise introduction for aspiring 'Data Scientists' and managers/founders/CEOs setting up a Data Science team.

[Leopoldo Corona · Rating 4 out of 5]

Brief summary of how the 'Data Scientist' role was born. Patil's ideal characteristics of a new hire for your data science team.

[Sanya · Rating 1 out of 5]

A lot of faff about nothing in particular. Data scientists should be people who are motivated, smart, and curious, and if you want to build a team of them - everyone should get along. That's it.

[Elvis Rodrigues · Rating 3 out of 5]

Apesar de distribuído como um livro, está mais para uma matéria ou post de blog. O autor apresenta os desafios de montar uma equipe focada em Data Science, e conta como os resolveu ao organizar o time do LinkedIn nesta área. Não oferece nada realmente inovador, mas apresenta interessantes insights acerca da divisão de papéis na área.

[Fermin Quant · Rating 3 out of 5]

An interesting read, but way too short and kind of lacking in details.

[Miguel · Rating 3 out of 5]

Fairly good introduction to the subject. A bit short, but with many pointers to further info on the emerging area of Data Science. The author shares many useful lessons learned while running the data team at LinkedIn. A good starting point for those who want to become acquainted with this rapidly growing field.

[Jesús Navarrete · Rating 4 out of 5]

Interesting story about how they create the first data science team in LinkedIn. He includes some details about the type of the profiles needed to create the team, how they was working, how it is better for data-products to work.

[Stephen Brownell · Rating 4 out of 5]

DJ Patil presents lots of good ideas in this free short book/long article about how to build a data science team, and what that term even means. Recommended for people working in analytical roles.

[Henrik Berggren]

Data team basics. A good check list for hiring data ppl in the middle.

[Sebastian Perez Saaibi · Rating 5 out of 5]

Simple account on what the basic structure of a data science team should be. Recommended as a quick, introductory read

[Jonas · Rating 2 out of 5]

A very high level overview building Data Science team based on LinkedIn experience. Nice quick read, but lacks details.

[Mike Fowler · Rating 3 out of 5]

Given it's age (8 years) the definitions have held surprisingly well. Some interesting ideas around hiring, especially the questions around "Would I run a start up with you?".