5 Qualities to Look for When Hiring a Data Scientist, 5 Qualities to Avoid

Learn Data Science
Teradata Employee

The most important decision your organization will make is who it decides to hire.

In this blog we will explore the five qualities that make up a great data scientist and the five that do not.  You may be surprised by some of the attributes as they aren’t going to be what you expected.  Having a 25 year career in solution development, I have had the opportunity to work with, train, and hire many great people.  I am sure you will have a different opinion and I welcome that, but let’s get started by exploring the 5 qualities that I believe make a great data scientist.

Five qualities to look for in a data scientist:

1.      CREATIVITY:  Creativity is by far the most important factor to consider when hiring a data scientist.  I have worked with many persons in the data field and it is the ability to solve complex problems and innovate that will get you across the finish line.  People who are creative aren’t afraid to fail, can change direction easily, and actually look at their failures as opportunities.  Big data is not about the statistics and results but about the STORY that the data supports.  Story telling with data takes amazing creativity and is a must have in finding a data scientist.

2.      PASSION FOR DATA/CHANGE:  I love data, I like hanging out with people who like data.  Data is everywhere and is a common part of our everyday lives.  We are constantly looking at it in some way.  It is in our bank accounts, our clocks, our cars, and just about everything you do.  Think for a minute about one thing that doesn’t have something to do with data?  Believe me, whether you know it or not, your entire life revolves around data.  So if you don’t love it, and you are surrounded by it constantly, then you are probably overwhelmed.  Finding people who love data is key to the success of your organizations data ambitions.

3.       GROUNDED IN REALITY:  In order to be successful in data science you must have your feet firmly planted on the ground.  You must know what projects are science projects and which ones are rational.  One of my mentors would constantly challenge me with defining use cases and if they were good candidates.  He would also ask what are good sources of big data that could have value to an organization?   Asking these questions will help you understand if a person is grounded in reality as well as if they are creative.

4.       DRIVE FOR RESULTS:  A great data scientists wants to get things done and move on to the next challenge.  They are curious about solving problems and coming up with interesting solutions to complex problems.  They do not want to sit on one thing for too long.  These people are not operators or people who want to manage an infrastructure.  These people love challenge and do not fear the problem, they will not get stalled when they hit a wall; they will find a way around it, over it, or through it.  They want to get it done.

5.       COMMUNICATION SKILLS:  A great data scientist can tell stories.  They can write, they can build presentations, they can speak in front of large groups and in the board room.  They can defend their results, and they can influence others to promote healthy change.  A data scientist should know how to promote healthy change which should be the result for any big data project; otherwise it is just a science project.

Now that we have explored the five qualities you should look for in a data scientist; we will now turn our attention to the five qualities you should avoid.  Again, you may have your own opinions and that is great.  Let’s get started.

Five qualities you should avoid in a data scientist:

1.        LOW EGO INTELLIGENCE:  Low ego intelligence is usually masked in an arrogant attitude.  Arrogant people are afraid of being discovered for some quality they are trying to hide from others.  They are generally rude, biased, and afraid.   They do not like to share their toys, ideas, or thoughts with other persons because they automatically consider their ideas as superior.  In reality people who display this quality are afraid of being judged and having their ideas shattered.  Who isn’t right, but it is a person that knows this and can deal with it that makes a great data scientist.

2.       IT’S GOTTA BE…..:  Fill in the blank.  These people are laser focused on something they want to learn for their own edification and pursuits.  I have been around countless data scientist’s and architect’s that “IT MUST BE HADOOP” or “IT MUST BE SAS.”   No, the rules are changing every day and so is the technology.  New innovating products are coming out all the time.  If you are very technology dedicated then you are missing out on a huge fun world, with problems that require more than one skill or tool to get the job done.  Lighten up Francis!


3.       TECHNOLOGY EAGER:  These people are ready to apply any technology without thinking about the two other major factors in a solution: PEOPLE and PROCESS.  Solving data problems takes the ability to operationalize an analytic output.  To do this you must be able to change BUSINESS PROCESSES and in order to do that you must be able to CHANGE PEOPLE.  If you are too technology centered you  must get out of that mindset and start to think about how people will use your solution to change process to improve some factor of your organization.

4.       LACK OF EXPERIENCE:  A good data scientist has some life experience.  They have traveled, they have worked for many different organizations, they are seasoned, and have felt the pain of failure and the joys of success.  They look before they leap because they understand the pain of a wasted experience.   So many people are coming out of college and entering the big data space and they lack the real world experience of knowing what makes sense and what does not.   There are always exceptions however and some very bright inexperienced people out there, however data science leadership takes experience.

5.       RISK AVOIDANCE:  To be a great data scientist you must have the desire to try new things and take some risks.  Taking rational risks is the only way anything is going to get done.  If you side on caution nothing gets done, these people do not do well with ambiguity.  They want a 9-5 job.  A good data scientist wants to light small controlled fires to ignite change in an organization.  They want to intelligently create new products, solutions, and carefully weigh the risks.

CONCLUSION:  How do you find these people?  Read their resume carefully.  Have they lived abroad?  Have they stayed at the same company all of their career?  How many industries have they worked in?  Have they owned their own company?  Do they blog or tweet about their job function?  When you do get to the interview process, can they articulate their past projects and life experiences?

Another tool I use is puzzles and games.  I will sometimes set a box of lego’s on the table and ask them to put the set together.  Some people look at me like I am nuts, maybe they are right!   Some people dive in.  I love that!  I watch what they do, how they do it, and if they struggle doing it.  I also look for methodologies and sorting of pieces by color or shape.  I am watching and evaluating their desire to take on a challenge, flexibility, and their problem solving skills.  If they are smiling while doing it!  Even better!

Most importantly “DO THEY LOVE DATA?”