Over the past 5 years, I have interviewed more than 1,000 candidate data scientists for an apparently highly coveted set of jobs at Evo Pricing. In the process I have learned that the media are portraying a fundamental lie about this profession: throwing data at off-the-shelf algorithms is really not the point.A fundamental rethink would be appropriate, and it is likely overdue.

70 years’ history in 2 paragraphs and 1 picture

At its heart, data science is a noble name for a broad set of number crunching activities that were mostly invented long ago, but recently received a new lease of life from being applied with greatly enhanced technical devices: more data, more processing power, more reasonable outcomes at a cheaper price.

The three waves of Artificial Intelligence according to Evo Pricing, based on DHL research

As the cost of storing and processing data went down, the volume of data collected went up: very simple law of supply & demand, or you can call it the Price Elasticity of Data if you will. Price goes down, volume goes up. Someone will then have to do something with all this stuff. Enter Data Science.

Common misunderstandings surround data science

Xcdk comic by Nicholas Oleh on blogspot (CC)

What is data science?According to Berkeley: _one of the most promising and in-demand career paths for skilled professionals._According to me, the name ‘data science’ suggests the particular approach of being a solution in search of a problem. _Here, some data; what can we do with it, anything?_Actually sounds sub-optimal, not only career-wise to set up one’s profession, but also as a business strategy: let’s invest big money to gather all this data, one day something good will come out of it.Unfortunately, the industrial revolution in the XIX century gave us schools and universities to train large numbers of blue collar workers to provide uniform answers to pre-packaged questions efficiently; and little has changed ever since.

What about training humans to ask the right questions instead, and letting the machines find the answers?

Data science can be a career dead-end

Even if many flavors of data science are gaining new popularity, like Artificial Intelligence and all other marketing hype that goes with it, the profession is mostly good for early tenure learners only.Good salary prospects of 80k+ yearly average may sound appealing, but averages hide the full complexity of the challenge. To truly succeed with data one must excel at specific, impactful and well-defined problems, rather than become a generalist expert of data or even worse science, which is mostly old from an academic point of view – as the opening image shows.Data and algorithms are powerful tools. But, like with any tool, they can only be as good as the use that one makes of them.

Developing Business Science to succeed

How can one become successful with data? Focus on the problem to be solved, the job-to-be-done, instead of the data.

For those who focus on for-profit use cases, Business Science suggests all the right ideas:

• Business problem to be defined, researched & solvedScientific, data-driven approachBusiness impact: measurable, objective outcome.

For not-for-profit and other use cases, the logic is nevertheless similar: start with question/hypothesis, use rigorous methodology, then go back to the learning critieria/question and validate if any impact was proven or not. Rinse and repeat without distraction.Now the problem is** how to get the job done**? So much we can directly learn about this, from an apparently hilarous analogy.

#careers #data-science #leadership #evolution #business