Data Scientists Personality Test

I. Basic Information on the Test

In a previous post, I suggested the use of a personality test to better categorize different types of data scientists and assign them to the best job for them.

I am not a psychologist, so I would suggest extra care and help in doing that, but I want to provide a basic test to understand the personality of the scientists belonging to your team.

This is the classification I proposed in the previous article:

The terminology used to classify into 16 subcategories the different kind of data scientists is given by the two-entry matrix exhibited in the table above. The terminology can be sometimes misleading if you have clear in mind the Keirsey Temperament Sorter (KTS), and this is why it is necessary to specify that the only categorization borrowed from KTS framework is the broader one, i.e. the Artisan-Idealist-Rational-Guardian partition.

Every sub-category has instead to be taken as newly generated. Here it follows the personality test to sort data scientists into a specific box. It is composed of 10 questions, and for each one, a single answer has to be provided.

Again, this test is not a professional temperament test to fully understand individuals’ personality (which I think to be almost impossible), but it is more a quick tool for managers to efficiently and consciously allocate the right people to the right task.

II. Questionnaire

1. When you start working on a new dataset:
   a. You start exploring immediately and querying the data
   b. Plan in advance how to tackle it
   c. You spent time in understanding the data, where they come from, and their meaning
   d. You identify a research question quickly and focus on designing a new improved method for analyzing your data
2. In your team, people count on you for your:
   a. Troubleshooting ability
   b. Organizational skills
   c. Capacity to reduce the problem complexity
   d. Strategic approach and conceptualization of the problem
3. When facing a new data challenge, your first thought is:
   a. Is what I am doing impactful and relevant?
   b. When do I have to deliver some results?
   c. How can this challenge make me a better scientist?
   d. What can I learn from this dataset?
4. In a data analysis, which is the most important thing to you?
   a. Results, no matter how you do achieve them, what strategy or technology you do employ
   b. To achieve a result in the correct way and with the right process or technology
   c. Attaining significant results in an ethical manner
   d. Reaching the outcomes through an accurate, replicable, and efficient procedure
5. If you have finished your required daily work, you would:
   a. Focus again on your analysis and try to find an alternative and innovative way to achieve your final goal
   b. Start with something else, even if this may involve staying longer at your desk
   c. Help a colleague in difficulty with his analysis
   d. Give suggestions and highlight weaknesses in your colleagues’ works for the sake of the team and of the business development
6. If you would have some spare time during your work, you would prefer to:
   e. Optimize existing technology for the whole company
   f. Improve your analysis
   g. Try to derive new insights from your previous analysis
   h. Understanding how to maximize the value of your analysis
7. It is your ‘data-dream’ of:
   e. Speaking about data with only engineers and IT teams
   f. Teaching data related contents
   g. Engaging with people who do not know anything about data science
   h. Persuading and convincing the business team of the opportunities generated by big data
8. You prefer to work with:
   e. Huge amount of structured data
   f. Any kind of data that challenge me
   g. Behavioral or social media data, or any unusual data
   h. No data in particular
9. If you would quit tomorrow your data science job, you would prefer to become:
   e. An IT manager or a software engineer
   f. A professor
   g. A consultant
   h. An entrepreneur
10. What characteristic of big data you value the most?
    e. Volume
    f. Velocity
    g. Variety
    h. Value

III. How to Read the Test

Once each question has been addressed choosing a single answer, the result is given by pairing the reply chosen more often within the first five questions (a–d) with the answer that appears more often in the last five (e–f), as shown in the following table.

Hence, if for instance in the first five answers b emerges as the predominant answer, while in the last five f is the median, the person considered is a Cruncher.

Note: the above is an adapted excerpt from my book “Big Data Analytics: A Management Perspective” (Springer, 2016).