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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Notes
- 1.
We will describe different data science roles later in this chapter.
- 2.
In general, higher up the ladder one is, more cautious stakeholders will be.
- 3.
See Chapter 15 for guidance on designing data products.
- 4.
In private sector, \(r\) is multiplied by a target profit margin, which is inappropriate for an internal consultancy.
- 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.
Note that these examples are specific to the United States. The experience in other countries may vary.
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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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DOI: https://doi.org/10.1007/978-3-030-71352-2_16
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