This document discusses data scientist profiles and provides guidance on building data scientist teams. It begins by establishing the importance of analytics for businesses. It then discusses the term "data scientist" and characterizes data scientists as having diverse backgrounds but being curious and asking important questions. The document outlines skills of data scientists and notes that while backgrounds vary, soft skills are very important. It provides tips for recruiting data scientists and emphasizes getting started with an analytical team even without perfect conditions.

1. Data Scientist Profiles
Tuesday May 19th 2015
By Dries Van Nieuwenhuyse
Prof. EHSAL Management School
Researcher at BICC Thomas More
Lecturer Strategic Management HoGent

2. Agenda
• Do analytics matter? Of course!
• What’s in a name: do we really need a new term ”data scientist” for this?
• Characteristics of data scientists
• How do we recognize them?
• How do we build up data scientist teams?
• Feasible?

3. Do analytics matters? Of course!
Who’s on Facebook?
Who’s on LinkedIn?
How much overlap is there in the PYMK suggestions? (20 – 40 – 60 – 80 – 100)

4. Do analytics matters? Of course!
« Some companies have built their very businesses on their
ability to collect, analyze, and act on data. Every company
can learn from what these firms do. »
Thomas H. Davenport, 2006. Competing on analytics. Harvard Business Review, January 2006 p99-107.

5. What’s in a name?
Whether employers know or don’t know what data
scientists do, they have been using -­‐ in rapidly
growing numbers -­‐ the term “data scientist” in job
descriptions in recent years...

6. What’s in a name?
What then are the data scientists, these new men and women of industry?
o Are they scientists?
o Engineers?
o Programmers?
o Business Controllers?
o Financial Controllers?
o A new breed of business decision-­‐makers and innovators?

7. What’s in a name?
• Google‘s chief economist Hal Varian commented in January [2009] that the next sexy job in the
next 10 years would be statisticians. By statisticians, he actually meant it as a general title for
someone who is able to extract information from large datasets and then present something of
use to non-­‐data experts…
• In June 2009 in a blog post titled “Rise of the Data Scientist” by Natahn Yau, a PhD candidate in
statistics, the term was first really used.

8. What’s in a name?
“What data scientists do is make discoveries while
swimming in data… [their] dominant trait is intense
curiosity - a desire to go beneath the surface of a problem,
find the questions at its heart, and distill them into a very
clear set of hypotheses that can be tested. This often entails
the associative thinking that characterizes the most
creative scientists in any field….”
Thomas H. Davenport & D.J. Patil (October 2012). Data Scientist: The Sexiest Job of the 21st Century.

9. What’s in a name?
A data scientist is an engineer who employs the scientific method and applies data-­‐discovery
tools to find new insights in data. The scientific method—the formulation of a hypothesis, the
testing, the careful design of experiments, the verification by others—is something they take
from their knowledge of statistics and their training in scientific disciplines. The application (and
tweaking) of tools comes from their engineering, or more specifically, computer science and
programming background. The best data scientists are product and process innovators and
sometimes, developers of new data-­‐discovery tools.
Data Scientists: The Definition of Sexy (Gil Press)
http://www.forbes.com/sites/gilpress/2012/09/27/data-scientists-the-definition-of-sexy/

10. What’s in a name?
A data scientist is a job title for an employee or business intelligence (BI) consultant who excels at
analyzing data, particularly large amounts of data, to help a business gain a competitive edge.
Margaret Rouse, WhatIs.com

12. How can we recognize a data scientist?
• Very different backgrounds
• Curiosity beyond day-­‐to-­‐day activities
• Bricolage versus engineering…
• Questions are more important than answers
D.J. Patil (2011) Building Data Science Teams. O’Reilly Media.

13. Skills of a data scientist
The significant problems we face
cannot be solvedby the same level
of thinking that created them.
If I had an hour to solve a problem and my life depended
on the solution, I would spend the first 55 minutes
determining the proper question to ask, for once I know
the proper question, I could solve the problem in less
than five minutes.
A. Einstein

14. Skills of a data scientist
• Finding rich data sources
• Working with large volumes of data despite hardware, software, and bandwidth constraint
• Cleaning the data and making sure that data is consistent
• Merging multiple datasets together
• Visualizing that data
• Building rich tooling that enables others to work with data effectively
D.J. Patil (2011) Building Data Science Teams. O’Reilly Media

15. Data scientist = BI professional?
EMC (2011). Data Science Revealed: A Data-Driven Glimpse into the Burgeoning New Field.

16. Data scientist = BI professional?
EMC (2011). Data Science Revealed: A Data-Driven Glimpse into the Burgeoning New Field.
• BI professionals focus on qualitative visualization of existing business data
• Data scientists apply advanced analytical tools to generate predictive insights
• More communication
• More scientifically trained
• Introvert – Extravert
• Bricolage -­‐ Engineering

17. Do data scientists need a PHD?

18. Changing role of controllers
• Gradient between Flexibility and
Control
• Gradient between Internal and
External focus
• Finance is moving in
Cornel, Renes & Vervuurt (2013). Controllers - Fit for the future. MCA

19. Can BI professionals become data
scientists?
• Nowadays everyone wants to become proactive, analytical
and strategically aligned
• Will they all succeed in this mental shift?
• Of course they won’t all succeed
• So there is plenty of room for talent
• Obviously a good understanding of the domain of
Performance MANAGEMENT in compleness will play a
pivotal role in this change

20. Can BI professionals become data
scientists?
• Sure they can!!!
• Own research shows that personal traits are in the end prevailing
• Not everyone needs to kick off the party
• Soft skills are more important than hard skills
• Hard skills are more obvious to learn
• Diversity of multi-­‐disciplinary teams is more important than individual skills
• Check for proactive and creative thinking

21. How to recruit?
• Focus recruiting the ‘usual suspects’ – (commercial) engineers, hard scientists, big data guru’s,
bricoleurs
• Scan memberships and active people in analytical and decision-­‐making communities
• Steal talent from Finance, IT, Marketing
• Actively scan LinkedIn
• Organize a contest for data science open to all profiles
• Did the candidate publish in magazines, books?

22. How to recruit?
• Test for creativity – ask for a possible research agenda for your company testing whether they are
actually prepared
• Organize for continuous job satisfaction and spontaneous evolution through the organization
• Let candidate work on a data set for a day, come up with proper questions and answers and let the
candidate present and convince an audience of decision-­‐makers
• Avoid overskilled and overtechnical PhD’s that can’t communicate

23. Doesn’t matter who takes the lead... just get
started
“Don't wait until everything is just right. It will never
be perfect. There will always be challenges, obstacles
and less than perfect conditions. So what. Get started
now. With each step you take, you will grow stronger
and stronger, more and more skilled, more and more
self-confident and more and more successful.”
Mark Victor Hansen