Hard-won lessons on building data science teams — hiring for potential over pedigree, skill assessment frameworks, mentoring junior practitioners, fostering data culture, and avoiding organizational pitfalls.
A practitioner's perspective on transferable data science patterns — how computer vision from forestry transfers to manufacturing, how fraud patterns cross domains, and why cross-industry experience is the ultimate career accelerator.
An honest account of independent data science consulting — scoping projects, managing client expectations, demos vs production, pricing strategies, and what 'production-ready' actually means.
