Why Are Data Science Leaders Running for the Exit?

The author is way off base in saying software engineering is about assembly rather than discovery. That's only true of the lowest-skill non-innovating software shops, and even then, developers are constantly having to discover how to use the new tool of the month.

On an unrelated note I don't really buy into the idea of data science as a distinct thing from computer science.

Data science is a horribly overloaded term.

Ask a databases person and they will tell you about ETLs and relational joins. Ask a statistician and you will hear about estimators and the bias-variance tradeoff. Ask a social scientist and they will tell you about experiments and causal inference. Ask a CS theoretician and they will mention streaming algorithms and probabilistic guarantees. Ask a machine learning person and you will probably hear a lot about prediction and stochastic gradient descent.

The approach that will breed the best practitioners is an interdisciplinary one; a great example is UBC's program: https://masterdatascience.science.ubc.ca/program/courses. And also future positions that separate the roles of data janitor, data analyst, machine learning engineer, and so on. There is a huge difference between what Facebook's Core Data Science team does and what most companies call "data science", which is sitting on a huge pile of poorly aggregated data and having little idea what to do with it.

Most importantly, data science has to be thoughtful and part of the engineering process, and can't be done after the fact with digital trace data. It's a lot easier to run experiments than it is to do statistical gymnastics with observational data.

This is an article making baseless generalizations that all data science is failing in Silicon Valley, and that the true need of a company is to hire a "data strategist." Unsurprisingly, the article is written by a self-proclaimed data strategist.

This blog post would be more compelling if there were citations or even general context backing up the authors pretty wild claims ("the vast majority (we are talking 80-90%) want to leave their current job", "failure rate of data science teams is over 90% right now", "the top 5 data scientists I have ever worked with, only 1 had a PhD").

As a data scientist working for a relatively well known startup in the Bay Area, I don't dispute that there are problems in this industry. There are many under-/un-qualified managers running around (I don't know if that's a data science specific problem, or if it also applies to much of engineering as well). There are ICs and teams that have trouble producing industry-applicable work because they're too academically focused. There are also business-driven teams that have trouble producing quality/reproducible work because they're too business focused.

That said, the claim of 90% failure is ridiculous on its face. Having another MBA put together a powerpoint presentation about a company's data strategy isn't going to suddenly solve the actual issues in data science.

Totally depends on what problem your trying to solve, what tools your employer have setup, and what data science you know how to do. The field is still not very mature, but as the tools get easier to use and companies understand how they can solve problems using machine learning and data science, and data pipelines are thought of at the beginning of projects, it will be a lot easier to apply more sophisticated techniques.