It’s 2020. Are you hiring Data Scientists or Analysts?

  • Statistics and analytics. Extracting meaning or value from sets of data.
  • Business and domain expertise. Translating between technical realities and business goals and constraints.
  • Computation and information systems. Developing software solutions to otherwise intractable problems around the storage and processing of data.
  • Projects take weeks to months instead of hours to days.
  • Data comes in unstructured, disjointed, or messy formats rather than ready-to-analyze sheets and tables.
  • Methods are specific and directed (e.g. context-appropriate statistical tests and techniques) rather than out-of-the-box or plug-and play (e.g. online tools and built-in calculators).
  • Datasets are measured in the millions or billions of rows rather than the hundreds or thousands.
  • Computer programming is required for deriving insights and value instead of general-purpose software.
  • Confirmatory. Conventional analyst role. Extracting value from challenging, complex, but ultimately well-mapped data sources using common tools and techniques to maintain consistency throughout the business.
  • Exploratory. Conventional data scientist role. Discovering value by discovering and defining unknown or unconventional data sources through a technically diverse, machine-augmented toolbox.

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