The Curious Case of the Data Scientist Profession

Data Science & the profession of a Data Scientist is being debated, rationalized, defined and refactored … I think the domain & the profession is maturing and our understanding of the Mythical Data Scientist is getting more pragmatic.

Now to the highlights:

1. Data Scientist is multi-faceted & contextual

  • Two points – It requires a multitude of skills & different skill sets at different situations; and definitely is a team effort.
  • This tweet sums it all
  • DataScienceTeam
  • Sometimes a Data Scientist has to tell a good business story to make an impact; other times the algorithm wins the day
    • Harlan in his blog identifies four combinations – Data Business Person, Data Creative, Data Engineer & Data Researcher
      • I don’t fully agree with the diagram – it has lot less programming & little more math; math is usually built-in the ML algorithms and the implementation is embedded in math libraries developed by the optimization specialists. A Data Scientist should n’t be twiddling with the math libraries
    • I had proposed the idea of a Data Science Engineer last year with similar thoughts; and elaborated more at “Who or what is a Data Scientist?
    • The BAH Field Guide suggests the following mix:
    • Data Scienc 03
    • I would prefer to see more ML than M. ML is the higher from of applied M and also includes Statistics
  • Domain Expertise and the ability to identify the correct problems are very important skills of a Data Scientist, says John Forman.
  • Or as Rachel Schutt at Columbia quotes:
    • Josh Wills (Cloudera)
      • Data Scientist (noun): Person who is better at statistics than any software engineer & better at software engineering than any statistician

    • Will Cukierski (Kaggle) retorts
      • Data Scientist (noun): Person who is worse at statistics than any statistician & worse at software engineering than any software engineer

2. The Data Scientist team should be building data products

3.  To tell the data story effectively, the supporting cast is essential

  • As Vishal puts it in his blog,
    • Data must be there & processable – the story definitely depends on the data
    • Processes & buy-in from management – many times, it is not the inference that is the bottle neck but the business processes that needs to be changed to implement the inferences & insights
    • As the BAH Field Guide says it:
    • Data Scienc 04
    • DS01

 4.  Pay attention to how the Data Science team is organized

5. Data Science is a continuum of Sophistication & Maturity – a marathon than a spirint

Let me stop here, I think the blog is getting long already …



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3 thoughts on “The Curious Case of the Data Scientist Profession

  1. Pingback: What or Who is a Data Scientist ? | My missives

  2. Pingback: The Sense & Sensibility of a Data Scientist DevOps | My missives

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