[reureu]

As a hiring manager in the data science/ml world in healthcare, I generally think of degree programs in "data science", "machine learning", "artificial intelligence", "deep learning", etc as being less valuable than degree programs in the corresponding fields that aren't as buzz wordy. I tend to prefer candidates from backgrounds like computer science, math, applied math, statistics, or something domain specific (coming from healthcare) like epidemiology (or variation like computational epi) or bio- or biomedical informatics.

Those programs tend to show me that you're interested in and have done the more boring but foundational coursework that is often cut to make the sexy degree programs. That means that hopefully you won't be upset that 100% of your job isn't deep learning, and that you'll be better suited to pick the right tool for the job.

At one of my last jobs, there was a machine learning engineering team (all boys) and a data science team (all girls and gays) who had the same ML chops. The DS team ended up getting more models into production and more research published than the ML team because they had more "soft" skills to navigate the problems the org was facing. When someone in leadership would say "we're having issues booking appointments", the ML team would set off building some fancy deep learning model while the DS team would generate hypotheses with stakeholders, do some exploratory analysis, run a few prospective studies, and then use those results to inform some regression models that would end up in production. It wasn't as sexy as some deep learning model, but the leadership team wanted full interpretability of their model so deep learning was never going to be acceptable. I generally think of these kinds of skills being taught more the stats, applied math, or epi programs than in the designer ML programs. ymmv

[garciasn]

Hiring leader for a DE/DS team at a Digital Marketing agency; I feel the exact same.

I strongly prefer folks from non-specific degree programs who come with a desire to learn as opposed to deep experience in a program tailor made for a specific niche subject where degree candidates learned on absurdly simplistic or unrealistic data and models.

The modeling itself is largely meaningless and simple to execute against. It’s the data and the insights that matter and I haven’t yet seen a niche designer masters program graduate who actually could show me a meaningful end-to-end project they were truly passionate about.

[hnthrwaway17283]

>As a hiring manager in the data science/ml world in healthcare, I generally think of degree programs in "data science", "machine learning", "artificial intelligence", "deep learning", etc as being less valuable than degree programs in the corresponding fields

What if the data science degree is essentially equivalent to a computer science degree? The program I was admitted to permits students to enroll in graduate-level computer science courses, such as algorithms, networking, and systems, which contribute to the degree requirements.

Would you still hold a bias against those individuals? The data science program I was admitted to is essentially a computer science degree augmented with some statistics and AI courses.

I believe it would be misguided to categorize all data science degrees under a single label and to discriminate against hiring from these programs based on false assumptions.

[reureu]

I'm obviously painting in broad strokes, and for sure "data science" as a degree has become more mainstream over the years. Personally, I tend to interview everyone I can fit into my schedule that's been handed to me by a recruiter. And I have hired grad school dropouts, bootcamp grads, no grad school, graduates of DS programs. There's so much variation across with all of these things that it's difficult to make a highly sensitive and highly specific rule based off education alone.

I'm just saying that I seem to have more luck with people coming out of those traditional programs. But, also, as you add more jobs to your resume, the specifics of your education matters less and less.

[hnthrwaway17283]

Thanks for the reply.

>I'm just saying that I seem to have more luck with people coming out of those traditional programs.

Would having a master's in data science and also a traditional undergraduate STEM degree be beneficial?

>as you add more jobs to your resume, the specifics of your education matters less and less.

In my case, I'm attempting to move from a non-technical role to a technical one, with my master's degree serving as my gateway into the field, since my previous job experience isn't relevant. Do you have any tips on how to make my resume stand out to recruiters?

[reureu]

There's really two parts to worry about: getting an interview, and then passing the interview.

The getting the interview part is difficult: some places will more liberally interview candidates, and others more heavily screen them. The three things that you can change to improve your odds here are tailoring your resume to use more words and phrases from the job description (trying to game any AI or human resume screener), networking to try to bypass the screening stage altogether, and casting as wide of a net as possible. Networking can be anything from having a social media presence, going to various forms of dev events, talking to friends about open roles they've heard about, or even cold emailing people you're interested in (although, if you do this, you'll probably have more luck asking to zoom/coffee for career advice than asking if they have a job available for you). And then, regarding the role you're targeting, I moved into increasingly technical roles starting as a data analyst-- I know not everyone would agree with this approach, but it worked well for me. I was a really technical analyst, who became a data scientist, who worked up the ranks, and then started moving between DE/MLE/DS roles. But this was also back in the days when "data scientist" was a new term, and before it got so watered down-- so maybe with title inflation, my original "data analyst" jobs might be "data scientist" jobs today? Anyway, my point is that I think it's easier to slowly slide in to your ideal role than it is to try to hop directly into it.

The passing the interview part ends up being so much about how you communicate and frame work that you've done. It sucks because this ends up inadvertently screening out really smart/good people that struggle with this kind of thinking (and screening in people who are good at talking but suck at doing e.g., many MBAs). But once you're talking to a live person, I think emphasizing how your degrees (both grad and ugrad) have really prepared you for exactly the role in front of you. You can also often take non-technical experience as evidence of certain components of the technical job requirements. Like, I worked in a restaurant when I was a teenager, and you better believe that prepared me to deal with many concurrent demands from many different sources, and required me to think on my feet about the priority/order of operations. So, when I was earlier in my career, I got really good at answering questions along the lines of "you know, I haven't done this work in a single role, but I have experience doing everything you're asking for over multiple roles..."

But it sounds like a lot of the issue you're running into is just getting in the door to begin with? Unfortunately, I think so much of it comes down to luck-- just keep applying to as large of a variety of jobs as possible, and network as much as you can.

[hnthrwaway17283]

Thanks for these detailed answers!

[relyativist]

I am an expert with medical imaging domain, and doing research on AI on medical data. However, recruiters in most cases skip my CV because of not relevant ML/CS degree or previous experience.

[melondonkey]

The cultural divide between ML engineers and “girls and gays” in data science is very real and in my experience getting worse. Good but rare when the styles can be brought together.

[randysalami]

I’m getting my masters in AI (undergrad in CS) but this was after a two year gap in the consulting world. When I graduate, I’ll have my masters and have reached senior. Glad to hear my soft skills will be a boon going into the field.

[iknownthing]

So the ML team and the DS team were both working on the same problems?

[reureu]

it was a very toxic environment. The DS team existed for years within the clinical org, and then the CTO decided they needed to do more ML so created an MLE team. Originally it was pitched as an engineering team to create the pipelines to enable more ML by whomever (including DS), but the team members were more interested in solving organizational problems... but generally weren't equipped (connections, time, skills, whatever) to actually do that.

So, some of the "working on the same problems" was intentional-- it would require effort from both teams. But the dividing line was nebulous. The DS team would have preferred to do all of the stakeholder work through building a model, and then hand a pickled model to the ML team to implement in production. The ML team would have preferred to have the DS team scope the problem and hand off to them to do anything involving any form of modeling. It was a total mess.

But I have never worked at an organization where this has gone well, so I don't think it was an issue specific to that org. If you're involved in data things, you want to do interesting work and there's only so much interesting work to go around. And, ultimately, the vast majority of organizations don't have a need for tons of people to be doing the really technical aspects of ML/AI/etc. SO much of the work is scoping problems, cleaning data, worrying about pipelines, etc... and so if OP or whomever is thinking they're going to waltz into a job and make the next version of ChatGPT, that's really unlikely with anything less than a PhD. Personally, I've found a pretty good home being able to interact with leadership to define nebulous problems and solve those problems with whatever tool is appropriate-- and my success has way more to do with communication/project management/scoping skills than with technical skills (although both are necessary)... and I think those skills are better fostered through the more traditional programs.

[iknownthing]

Yeah that's pretty much my experience as well. I've also seen a lot of bait-and-switching going on where orgs tell candidates that they will be working on the cool interesting work, but then never deliver.

[willsmith72]

sounds like the ML team needed a good product manager, or ML engineers who think like product engineers