Hiring
Hiring for analytics is harder than it used to be. The blend of skills that matter — business sense, data engineering, statistics, and now the ability to work with AI and agentic tools — keeps shifting. This part helps you hire for the analytics team you actually need.
Start with the work, not the job title
Before you write a job description, be clear what the person will own. Map the decisions and use cases your team supports, then ask: is this hire filling a gap in coverage (we need more capacity for X) or in capability (we need someone who can do Y that we can't today)? That distinction drives whether you hire a generalist analyst, a specialist (e.g. ML, data engineering), or someone who will orchestrate agents and interpret outputs.
Skills that matter now
- Business alignment — Can they connect analysis to decisions and stakeholders? This hasn't changed.
- Data fundamentals — Understanding of data quality, pipelines, and how to get from raw data to something trustworthy.
- Statistics and modelling — When to use which approach; how to interpret and challenge results.
- Working with AI and agents — Comfort with agentic tools, prompt design, and knowing when to lean on automation vs when to do the work themselves. You're not hiring for "AI experts" only; you're hiring people who can use these tools to multiply impact.
What to look for in interviews
- Portfolio and past work — Concrete examples: what question they answered, what they did, what changed as a result.
- Judgement over jargon — Do they explain trade-offs and limits, or hide behind buzzwords?
- Curiosity and learning — How they keep up with the field and how they'd approach something they haven't done before.
Hiring for analytics is less about finding the perfect CV and more about finding people who can own outcomes, work with your data and your business, and adapt as the tools evolve.