VP of Data & Analytics
Recruit, develop, and retain data talent
What You Do Today
Build and manage teams of data engineers, analysts, data scientists, and ML engineers in one of the most competitive talent markets. Create career paths and a culture that attracts and retains top talent.
AI That Applies
AI tools that augment data team productivity, letting you do more with fewer people and making roles more interesting by automating routine work.
Technologies
How It Works
The system ingests candidate data — resumes, assessments, interview feedback, and historical hiring outcomes. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
The data professional role evolves as AI automates more routine analysis and engineering. You need people who can do what AI can't — frame problems, build relationships, communicate insights.
What Stays
Building a team culture of intellectual curiosity, rigor, and business impact. Retaining top data talent requires purpose, challenge, and growth opportunities.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for recruit, develop, and retain data talent, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long recruit, develop, and retain data talent takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your board chair or lead independent director
“What's our time-to-fill for the roles that are hardest to source, and where in the funnel do we lose candidates?”
They shape expectations for how AI appears in governance
your CTO or CIO
“How would we validate that an AI screening tool isn't introducing bias we can't see?”
They own the technology infrastructure that enables AI adoption
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.