#Dsci

Hiring is hard. Hiring data scientists (and MLEs) is harder. This has been my experience building major data teams on a couple of continents and what I advise you actually look for in Individual Contributors and Managers from a vetting perspective.

The Problem

Why is it so hard to hire for data scientists? There’re a few reasons.

1. Lack of clarity on what a data scientist actually is and does
2. Easy to bullshit (few non-data scientists can connect their work with outcomes vs outputs)
3. Obfuscation
4. Actually a basket of skills, rather than just one

What People Normally get Wrong

On the bad old days of everyone trying to do “big data” you’d often see desperate CIOs or similar in large companies, basically hire a bunch of PhDs and throw them in a corner with some vague direction to “do data science.”

[This is part 3 of a series on managing Data Science teams based on hard won experience running one of the larger data teams in SouthEast Asia from one of its unicorns. YMMV and advice here should be sanity checked to make sure it’s appropriate to both your corporate structure, culture, and situation. No solution is one-size, fits-all. This is to guide CDOs, VPs, and data executives and give an alternative viewpoint on organizing.]

This is part 2 of a series on managing Data Science teams from the trenches and based on hard won experience running one of the larger data teams in SouthEast Asia from one of its unicorns. YMMV and advice here should be checked to make sure it’s appropriate to both your corporate structure and situation. No solution is one-size,fits-all. This is to guide VPs, CDOs, > and data executives and give them a possible alternative viewpoint on unambiguous wins for our teams (and may help you and your organization.).

This is the first of a series posts on lessons learned in running large data teams and large-scale data projects in SE Asia. This first post focuses on organization structure, the second on foundational practices, and later posts will talk about way you can improve your own teams as they grow.*

Not too long ago, Data teams were a new, novel thing when suddenly everyone wanted to “do AI” and “Big Data” and no one knew how to hire or what to do. A common approach was to hire a whack of PhDs with quantitative backgrounds, put them off in a corner, and have them do “Data science” expecting magic to somehow happen.