Episode Summary: A few years ago I promised you a blog series on how to start your own consulting business in machine learning: getting set up, figuring out legal & intellectual property rights, finding consistent work, scoping in the face of research uncertainty, the project life cycle, mistakes to avoid...
I gave a few talks on this topic but never got around to writing that blog series. Today I sat down with John D. Cook, a fellow top ML/stats consultant for companies like Amazon, Google, Microsoft or Amgen, and we finally got to discuss (some of) that: John's background, his consulting for pharma and legal, project pricing, the best tool for the job vs. the cost of moving across tools, the Python language and its community, and more.
Of interest to people with a vicious streak of independence.
Links & resources:
John's consulting business: johndcook.com aka Singular Value Consulting.
John's instantly relatable, quirky and fun writings on his personal blog. RECOMMENDED! To get a taste, check out Don't invert that matrix, R language for programmers, Statistical distributions chart / cheat-sheet, Bayesian consulting...
So you want to be a data science consultant (or hire one)? (my presentation from Berlin Buzzwords 2015) and From Research to Industry, in Ten Not-So-Easy Steps (similar presentation from the Industry panel at SIGIR 2016).
Poll: In our chat, we barely scratched the surface. Which side of consulting is the most interesting to you? Let me know in the poll below and I'll cover it more. Thanks!