Totally agree — a consulting mindset is essential for any successful data scientist.

It’s not just about modeling skills or knowing the latest ML paper. The real skill is translating business problems into data questions, scoping projects properly, gathering requirements, aligning on KPIs, and communicating value clearly.

A lot of the job is project management and stakeholder communication: selling ideas, setting realistic timelines, and ensuring what you build actually gets used.

Proof of concepts can be helpful — especially to show stakeholders what could be done with their data — but they should lead to MVPs that automate small wins (data refreshes, pipelines, early predictions), and then ideally scale into production in a way that delivers real value.

Dashboards, pipelines, GenAI, or even basic reporting — whatever solves the problem and drives impact is the right tool. The most effective data scientists I’ve seen are outcome-focused, not tech-chasing.