What background/skills do you need to qualify for "data scientist" roles as opposed to ones as a "data analyst?" I have a PhD in a data-heavy social science and am an advanced R user, but I worry that my background isnt sufficiently technical to target data scientist positions. If not, is it a matter of building out my Python skills?

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Short answer: You’re fine.

Longer answer: DS typically does more modeling and problem framing /requirements translation than data analyst, which tends to be either more junior or more purely focused on data engineering or reporting.

Sincerely, another PhD who likes industry over academia

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Academia is such a racket, always happy to meet one of my people.

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Don’t let anyone fool you, you’re much more of a data scientist than you think. All you need is a project to formalize it.

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Some things to think about from a mid-30's frequentist trained, bayesian in belief, statistician with "just a masters and a pinch too much measure theory":

A Data Scientist is someone who can frame problems, propose and execute solutions, analyze data to size an opportunity, provide guidance, and next steps.

A Senior Data Scientist (typically) does what a Data Scientist does and goes farther to provide several solutions to a problem with varying complexity of execution, typically provides mentorship, and often works to craft/shape the discourse.

A Lead DS (often) does the above plus, or often instead of delivering execution, provides a menu of available options with carefully crafted risk/benefit to each solution to leadership, while steering a set of larger scientific discussions, leading research initiatives, guiding multiple projects across multiple resources towards several program and business objectives.

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Why does any of that matter? Data Science LEADERS do not really care about the tooling you use to execute the job - they care that you have built something to show results.

I have hired a lot of DS's, statisticians, researchers, and scientists - I DO ask what tools they are familiar with to accomplish the job, then I craft the rest of the conversation around feeling out their competency with that toolset. From there, I dig in to their scientific knowledge base.

Note, that is not specific to a programming language, algorithm, or what ever the flavor of the month is with Data Science at large as LinkedIn would have you believe.

Youve already done the hard part, you are a scientist. Pick up a new language, build a portfolio, show results, keep track of metrics and improvements you make to things - those become talking points as you "progress".

Always happy to connect and discuss more.

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I’m not a data scientist but work in a data field and have a quantitative masters (Chem Eng) which had me do programming also. From my research since I’m also interested in the job R is heavily used but python seems to be mentioned on more Job listings particularly because of the libraries available so I’d recommend diving into abit of that and you could do SQL also but that would be really easy if you have already got coding experience

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For a starting position yes - since you have a really great scientific background and I assume substantial knowledge on statistical methods etc it’s more applying those with programming you already have the “knowledge” part and of course reading up on the latest literature on algorithms etc. you should check out things like kaggle I’ve also got a tonne of books on pdf for this stuff if you’re interested DM your email and I can send them to you

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As someone who has an MS in a physical science and got hired as a Data Scientist I am confident that you will be fine. I too left academia and am very happy with switching.

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Advanced R is great, but most positions require Advanced Python

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Absolutely go to Python and SQL next, in that order. Python shouldn’t be hard to learn after advanced R. The worst part will be learning all the libraries you need to use for specialized things that R has built in.

You're missing something for some roles, but not all. Interview around. They're fewer, but there are teams that work mostly in R. And so long as you can read python, you'll be ok on language agnostic teams.

One thing your resume is probably short on though is databases and cloud. Minimum is SQL, but you should be able to pick that up quick (few weeks). More granular stuff like different DB's and cloud services is a plus, but not essential.

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That's super helpful feedback, think you make a lot of great points! You're right on the database stuff, definitely need to get some experience in it.

Hi OP, I work on a team with data scientists. Are you currently looking/interviewing?

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Wouldn't quite say I'm interviewing/looking at this point, more just trying to understand the lay of the land for the future.

What is your PhD in OP?

My understanding is that you basically have to do it full time in most contexts. It's possible to do it part time but it is MISERABLE and takes forever (I know someone who's been at it 10+ years). European universities are a bit different and work better if you have a masters already, but you also don't get the same kind of training as you do in an American program generally speaking.

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