That's perfectly fine but it's not what traditionally is referred to as data science. I'm actually quite annoyed at what has been happening to the term data science lately - it's supposed to be some stats-heavy/applied-AI role but a lot of companies hiring "data scientists" are really just hiring SQL jockeys.
Personally I've done both data science and data infrastructure and I like infrastructure a lot more anyway. And it sounds like you are somewhat qualified for that with some of your pipeline work (although big data experience is also important). A LOT of data science departments have no idea what type of business value they are supposed to be adding, are doing shitty boring work with glorified titles, or are improperly integrated with the company at large (bad productionizing processes, poor data infrastructure). There's always going to be a need for data infrastructure but the "data science" hype is going to fade once all the shitty data departments cut the fat.
When I interview people who have your type of background, I tend to get confused by what exactly it is the person wants to do (Analyze Data? Build an Analytics Pipeline/Architecture? Write Software/Services? Be an Analytics IT person?). Sometimes they have to talk about all the "cool" stuff they've done and lose focus on what they bring to the role. I also become skeptical because it's really easy nowadays to follow a few tutorials on 50 different things and then boast about how you did it all yourself.
Even reading your comment, you don't sound like somebody who wants to analyze data.
As far as being a generalist, I definitely agree that it's good to have somebody with skills in ETL, analyzing data, and maybe building a software service. But what happens is that all those things happen at different speeds and then people get crushed. You're asked to investigate some data quickly over 3 days, but suddenly the software service you built is having issues and you need 2 weeks to dig in and fix it, and also your ETL job is overloading the server you need to fix it yesterday but you need help from a somebody else to figure it out. Oh, and that Metabase thing you installed is broken and the VP was using it and has a big demo tomorrow.
But this is the crux of the job-seeker's dilemma. If he/she is specific about their interests when speaking to an interviewer, they might get a response like "well, we're really looking for someone whose operational focus is [something else]".
And if they're not super-specific (I doubt anyone does data analysis exclusively without any other involvement in the project), but instead attempt to give examples where they had demonstrable impact working across a number of domains, you might hear a response like this:
> Even reading your comment, you don't sound like somebody who wants to analyze data.
But if you have a resume (or say this during an interview) that gives equal weight to the data analysis and the stack deployment, it's just confusing to the person reading it. Especially in Data Science, which already confusing from a skillset perspective. Lots of resumes look like the applicants just thought 10 things with minimal overlap were cool and decided to put them on their resume.
Even if you did work at a 5 person startup and had the unoffical title of "Data Scientist, Data Engineer, Data DevOps, DB Admin, and Chief Data Officer" I'd recommend you downplay some of those based on the jobs you are applying for. Figure out what is essential and what is +1
But you realize that the vast majority of businesses in North America need someone to solve all of those problems. They aren't going to hire and cna't afford an experienced data team of specialists.
This is the point of the article. Getting the 80% is far more valuable than having some PhD optimizing the hell out of features. Silicon Valley tends to overthink things.
The vast majority of business don't need Data Scientists. They need a BI person with SQL skills and some of the skills of a Database Admin. What most companies really need is a good set of Dashboards and clean data to feed it. This enables the business people to get the information/visibility they need an make decisions.
Also, most businesses should not be building analytic services and deploying them - they should be paying for a good product with a cloud or easy on-prem install and getting support from the company that sells the product. A few licenses of a good BI product are a lot cheaper than a Data Scientist.
When I am up and running I don't want yet another generalist - or rather I will happily take one, I just will put them in a box making pins.
Perhaps the GP will do better at the consulting level - or even some level of productise consulting - and out of the box product
The trouble you might be having in getting an interview is probably partly to do with your background and likely also that those job postings get A LOT of submissions. Other hiring managers in my department as well as myself have found we get 10x more submissions for DS/ML positions than software dev positions. In general it's a really unrefined and new job skills that anyone and everyone who's taken a coursera course in regression or clustering will apply.
When I'm hiring I care most about wins. These are wins. When I read 30M events / month I want to hear more.
The rest is fluff IMO and things that I'd expect you to play around with while you're self learning. Also, most hiring managers don't care about what you did 10 years ago in sales if you're applying for a data science role. It might be icing on the cake you can share if you get into a conversation about sales or marketing, but otherwise it can feel off topic.
I'd slim down your resume to focus on these two wins (plus any recent experience building or leading teams) and stay later focused on recent data science related work in production. That sounds like a good enough resume to get an interview at most companies. Good luck!
Losses can be just as important, IME. “I tried to do X, tried several methods, each one failed due to...” would be an interesting conversation to have with an interviewee.
Check out the series by Jeff Leek, Brian Caffo and Roger D. Peng, “A Crash Course in Data Science.”
Hope this helps, I can be pretty clueless sometimes so you probably already know all the mathy bits.
This certainly doesn't excuse hiring managers from lazily filtering out candidates who don't have these things. But these are strong signals.
The skills you have are useful. I know we wouldn't hire you in for a data science role on my team. I could imagine many other places they're useful, though, so perhaps it's the places you're searching.
You can also train entry level for a good amount less too.
Email me: mark at dotscience dot com
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It’s not, though. I know a data scientist who studied Arabic language. And software engineers who studied music.
Your comment reads more like sour grapes about your own college experience than anything else ;)