Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Do good: strategies for leading an inclusive data science or statistics consulting team

Published

Author(s)

Christina Maimone, Julia Sharp, Ofira Schwartz-Soicher, Jeffrey Oliver, Lencia Beltran

Abstract

Leading a data science or statistical consulting team in an academic environment can have many challenges including institutional infrastructure, funding, and technical expertise. Even in the most challenging environment, however, leading such a team with inclusive practices can be rewarding for the leader, the team members, and collaborators. We describe nine leadership and management practices that are especially relevant to the dynamics of data science or statistics consulting teams and an academic environment: ensuring people get credit, making tacit knowledge explicit, establishing clear performance review processes, championing career development, empower team members to work autonomously , learning from diverse experiences, supporting team members in navigating power dynamics, having difficult conversations, and developing foundational management skills. Active engagement in these areas will help those who lead data science or statistics consulting groups – whether faculty or staff, regardless of title – create and support inclusive teams.
Citation
Stat
Volume
13

Keywords

data science or statistical consulting team, leadership, management, inclusive team

Citation

Maimone, C. , Sharp, J. , Schwartz-Soicher, O. , Oliver, J. and Beltran, L. (2024), Do good: strategies for leading an inclusive data science or statistics consulting team, Stat, [online], https://doi.org/10.1002/sta4.687, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957320 (Accessed November 21, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created May 12, 2024, Updated September 16, 2024