I would suggest looking at the Certified Analytics Professional certification from INFORMS. Operations Research is the original data science, and preparing for and passing the test is a good signal that you understand analytics.

Machine learning is just one tool in the arsenal of providing value using data.

As opposed to a regression-and-printf monkey (sorry, "Data Scientist")?

Like it or not - a lot of companies run on dashboards. To, you know, actually get their data out to people. So they can run their business, so you can get paid.

If you dig math and like doing mathy things, good for you. There's no need to be snide about the work that others do that (like it or not) as just as essential to keeping the business afloat as yours.

You are absolutely right though that pigeonholing in this industry is very much a thing. And intellectual honesty, not so much. Lesson being -- if one is targeting certain fields, it is often best to leave certain things off our resumes (or tone them way down).

At the end of the day, the point of corporate analytics is to provide actionable insights to improve a company’s profitability.

Descriptive analytics are the lowest form of analytics. If it is appropriate for your problem, and if you have the brainpower to do it, I am much more in favor of prescriptive analytics.

What’s better:

1) a dashboard that shows me inventory levels of various commodities at dozens of locations, from which inventory management employees are supposed to come up with a resupply schedule.

2) an optimization model that is specifically formulated for this problem, that runs every morning at 7am, with a quick double check (indeed, maybe visualized in a dashboard) of the output by a skilled human.

A company that uses the latter approach will dominate a company that relies on humans staring at graphs and charts.

If it’s a regular decision, it should be automated (with human double checking, absolutely). If it’s an irregular decision, a slide deck with matplotlib or similar visuals is fine. If that decision becomes more frequent, it should be built into an automated tool so humans don’t have to sift through dozens of charts and tables to come up with what will still be a suboptimal solution.

So perhaps I should have clarified my initial point. Most dashboarding that I see is purely descriptive. Descriptive analytics are neat, and absolutely have their place in exploratory data analysis and sense making about the basics of business, but are horribly slow to translate into actions as they must be filtered through humans who are getting deluged with visual information.

This reply is getting long, but it comes down to “what is the role of an analyst”? In my opinion, an analyst should tell me, the executive, what I should do. Or they should build me a system that makes this decision for me regularly. What I don’t want is to have an “analyst” merely present me with graphs and tables of my data. If you are not actually doing anything but visualizing the data, you are not analyzing it, and are therefore not an analyst. A data visualizer, or BI developer, but not an analyst.

A lot of people (including people whose job title contains the word 'analyst') think this.

PhD is seem as a magical badge of super power, even if you can get a job without one, consider having it, is a 1.5x-2x salary multiplier.