Data science is a position in tech blessed with the problem of asking “what should I do today?” Even if you have what appears as a directive — for instance “analyze this experiment’s impact under the global metrics” — you still have the prerogative to determine the level of detail the analysis requires. Should you simulate the projected lift from personalizing the experiment? Should you pull additional metrics? Should you understand why this experiment moved metrics and suggest additional experiments?
In short, the choice of what to do includes:
- Pull top tier metric impact (daily duty of a Data Monkey)
- Decide which additional metrics should be pulled
- Simulate customizing treatment based on demographics
- Understand why this experiment moved metrics and suggest followup (signature of a Unicorn – a person with the 30 skills listed at the bottom of a data science job description)
An unconscious choice is now made. The analyst immediately filters the options to what their own skills are capable of. If they have a mathematical reasoning limit, they will not think there are additional pertinent metrics. If they can not write the hadoop necessary to pull member demographics, they will not think to customize the experiment. Now for the tricky one: If they are not a unicorn, they will not ask why, have the je ne sais quoi to discover why, and ultimately propose additional wins!
Now for a given experiment, the difference between a data monkey and a unicorn, may not be that large. Even over the course of a dozen experiments the relative gain on moving company metrics may only be 10%. But, every now and then, the decision to be made is the choice between two possible products that will determine the course of the company. I leave to the imagination the differences in company success when the analysis of which product directions to develop is done by a data monkey or unicorn.
The silver lining is twofold: with experience you can discern which projects warrant unicorns or data monkeys and unicorns are made, not born.