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Building Data Teams

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Part of the Springer Series in the Data Sciences book series (SSDS)

Abstract

Like in private sector, the promise of data science swept government by storm. Teams were recruited under a simple assumption: good things can happen if you give smart, eager people some data. Indeed, the earliest data scientists in public sector had the opportunity of a lifetime to explore untapped datasets and affect change. But the technical competence alone was not enough to affect change.

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  • DOI: 10.1007/978-3-030-71352-2_16
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Notes

  1. 1.

    We will describe different data science roles later in this chapter.

  2. 2.

    In general, higher up the ladder one is, more cautious stakeholders will be.

  3. 3.

    See Chapter 15 for guidance on designing data products.

  4. 4.

    In private sector, \(r\) is multiplied by a target profit margin, which is inappropriate for an internal consultancy.

  5. 5.

    In some organizations, statisticians also have data scientist titles. Computational statisticians are a special case that rely on machine learning to draw inferences and have many overlapping skill with “full stack data scientists”.

  6. 6.

    Note that these examples are specific to the United States. The experience in other countries may vary.

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Correspondence to Jeffrey C. Chen .

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Chen, J.C., Rubin, E.A., Cornwall, G.J. (2021). Building Data Teams. In: Data Science for Public Policy. Springer Series in the Data Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-71352-2_16

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