Have you tried paid services/consulting arms of software or cloud companies? Teams that bill customers at an hourly rate? They generally look for generalists who can help customers tackle problems at different levels of the stack. They aren't looking for PhDs in statistics.
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.
> 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?
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.
I don't think a person has to be super specific or say "I am interested in X and Y." What they do need is consistency in a resume so the reviewer/interviewer can evaluate what their primary and secondary focus areas are. Sometimes you have to leave stuff out. For example, Data Scientists typically don't deploy and maintain a Data Science stack unless it's a really small company. And a Data Science Infrastructure person at a bigger company probably isn't analyzing data unless they are just playing around to validate their stack.
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
>what exactly it is the person wants to do (Analyze Data? Build an Analytics Pipeline/Architecture? Write Software/Services? Be an Analytics IT person?).
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.
>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.
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.
I don't see this as a bad thing - it's a lifecycle thing. I absolutely would want someone like the GP to start my data team from day one - there is a lot to build and much to hang together.
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
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.