I find that Data Scientist and Controllers share a lot of similarities in their work. We could even say that the controllers are the Data Scientists of Finance.

I found valuable insights in the article “What The Data Scientists Really Do” from the Harvard Business Review which confirm the similarities.

Here are the parts I found the most interesting in this article:

Sure, machine learning and deep learning are powerful techniques with important applications, but, as with all buzz terms, a healthy skepticism is in order. Nearly all of my guests understand that working data scientists make their daily bread and butter through data collection and data cleaning; building dashboards and reports; data visualization; statistical inference; communicating results to key stakeholders; and convincing decision makers of their results.

The skills data scientists need are evolving (and experience with deep learning isn’t the most important one). „Which skill is more important for a data scientist: the ability to use the most sophisticated deep learning models, or the ability to make good PowerPoint slides?” He made a case for the latter, since communicating results remains a critical part of data work.

One result of this rapid change is that the vast majority of my guests tell us that the key skills for data scientists are not the abilities to build and use deep-learning infrastructures. Instead they are the abilities to learn on the fly and to communicate well in order to answer business questions, explaining complex results to nontechnical stakeholders. Aspiring data scientists, then, should focus less on techniques than on questions. New techniques come and go, but critical thinking and quantitative, domain-specific skills will remain in demand.

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