When I try to explain what a people analytics team does, I tend to start by describing the three1 key roles I’ve seen in larger PA teams.
The analyst is typically an expert on scaling data access and understanding to a wide audience. This can include building dashboards, formalizing definitions and other data governance work, or even creating in-house business intelligence tools.
The researcher/data scientist is typically an expert on things such as programming, automation, statistical methods, machine learning, employee selection/hiring methods, survey design, inferential research and statistics, qualitative research, etc., etc.
The partner is typically an expert in stakeholder management, communicating ideas, writing narratives/reports, prioritization, project management, data visualization, and similar skills.
In practice, these roles all overlap a lot — all three are likely to have good data visualization skills, researchers and analysts may be great with stakeholders, partners may be strong at programming, and so forth. In smaller teams, one or two people will be covering all these roles together, or perhaps a manager will lean more into the partner role while the rest of the team focuses more on the other roles.
I tend to share these role descriptions because answers to questions such as “what education should I get to land a job in people analytics?” depend a lot on what slice(s) of the people analytics team you want to prioritize. The path to becoming an analyst will differ from the path to becoming a data scientist, even if there is some overlap along the way.
I’m sure there are far more than three, but I find this broad categorization useful. Data engineering is also a key role, but not one I’ve included here.