The Major Flaw with Data ScientistsCan the notion of a data ‘scientist’ actually be detrimental?Dominik HaitzBlockedUnblockFollowFollowingApr 17Photo by Lucas Vasques on UnsplashThis post is about two things a (data) scientist typically comes along with: a scientific mindset, which is incredibly valuable, and an “academic working style”, which can be a major handicap.
The scientific mindsetA well-grounded scientific education equips a data scientist with important traits:Analytical thinking, to discover insights deeply hidden in dataHealthy skepticism and rigorous hypothesis testing, to deliver high-quality resultsUnderstanding concepts like statistical uncertainty or the difference between correlation and causation, thereby preventing clients or executives from drawing false conclusionsAs such, a data scientist’s skills are immensely useful for digital businesses.
The “academic working style”In academia, a typical PhD thesis goes like this:Pick a narrowly defined problemFocus on it for yearsFind the best humanly possible solutionFinally publish, but only after extensive reviewsA large portion of data science recruits have been through this process, and it has inevitably shaped their professional behavior.
Something similar is true for ”Generation Kaggle“.
However, the “academic working style” often clashes with the requirements of today’s digital economy.
Especially in an agile environment, a different behavior is necessary for success:Care about the product as a whole, not just the tiny modelling part you were assigned toKnow that “good enough” is often sufficientPut working prototypes before ultimate model performanceRelease early and often to field-test your solutionOf course, this doesn’t mean not to double-check your results, or release head over heels.
Also, there’s a difference between fast-paced digital product development and e.
working in a large company’s fundamental research department.
Conclusion: Keep the scientific mindset, but be agile about it!Do you miss anything?.Disagree entirely?.Share your opinion in the comments!.. More details