Change Management & Insights Lead

Capital Group•Los Angeles, CA

4d•$159,354 - $254,966•Hybrid

About The Position

We're asking Capital to change how we work, right now around AI and data, and next around whatever comes after. The question is always the same: are we actually evolving the way we intended to, and how do we know? This role leads our measurement operation, bringing together metrics, analytics, and research to turn signals about associate and leader behavior into an honest read on what's changing, where progress is stalled, and why. Those insights inform our messaging, storytelling, and experience work, and give senior leaders what they need to accelerate. This is a thinking job, not a reporting job.

Requirements

You understand Capital deeply. You know how this place really works. You can't do this role well from the outside.
You're a researcher at heart. You come from UX, design, or user research, or you work like someone who does. You're genuinely interested in why people change, and you know the difference between tracking activity and tracking real behavior.
You research at AI speed. You use AI tools to plan studies, synthesize interviews, and spot patterns fast, so one person can cover what used to take a research team.
You bring product thinking to insight. You start with the decision someone has to make and work backward to the data.
You can tell the story. You make what you find land with a leader who has five minutes.
You're comfortable with the tech and the numbers. You work with usage data, survey data, and dashboards confidently, and you understand enough about the AI tools to know what the data is really telling you. Not the deepest data scientist in the building, but not afraid of any of it.
People open up to you. You interview well and build trust fast. They tell you the thing they would never put in a survey.
You handle sensitive information well. You evaluate and address privacy and confidentiality concerns thoughtfully when working across departments. You never just hand over data. You tell us what it means.

Responsibilities

Design and run how we measure real behavior change across the firm, in partnership with the Enterprise AI Change team. You own pulling it together, keeping it alive, and making it sharper over time.
Treat measurement as a product, not a report. Know who uses it (leaders, the Enterprise AI Change team, product owners), what decisions they're trying to make, and build what helps them decide.
Strengthen our listening system by combining qualitative and quantitative research, drawing on signals we already have, including Voice @ Capital survey data as a read on how associates feel and how they're shifting, and bringing in new signals where we have gaps.
Own the roadmap for our listening system: which signals to build next, which to retire, and why.
Figure out how we measure leader behavior specifically. In partnership with BKSP, define what good AI leadership looks like in practice and build meaningful ways to track the leader behaviors that influence adoption and culture change.
Use user research methods (interviews, observation, journey mapping, and quick tests) to uncover the real story behind the numbers, build trusted relationships with associates and leaders along the way, and translate findings into recommendations people can act on.
Partner with the Enterprise AI product, analytics, and platform teams to connect communication, sentiment, and usage data into one view of adoption.
Stay curious about how the outside world measures this. Good outside thinking separates activity from real change: logins, licenses, and training completions tell us people have access, not that they work differently now. Bring in useful approaches from other companies and researchers and adapt what fits Capital.
Turn data into clear stories and guidance that shape campaigns, sharpen message development, and help leaders make confident decisions.
Start small and ship early. Get a first read in front of leaders quickly, then improve it based on what they actually use.
Scale yourself with AI, not with headcount. This role does not flex through contractors, because the value is internal judgment, trust, and institutional read, which cannot be outsourced. Use AI tooling aggressively to expand what one person can cover: synthesizing qualitative comments at volume, spotting patterns across surveys and usage data, and drafting first-cut analysis so your time goes to interpretation and to the conversations that surface what the numbers miss.