You’ll structure ambiguous problems, design experiments, identify confounding factors, challenge misleading conclusions, and build production systems that produce reliable outcomes repeatedly—not just one-off answers. This means owning the AI analytics harness around the analysis—including semantic layers, context retrieval, evaluation infrastructure, validation gates, and governance—so decisions remain reliable, contextualized, and trustworthy at scale.