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Some Reflections on Being Turned Down for a Lot of Data Science Jobs

tdhopper.com

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Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs

#91

So you're at least occasionally getting actual feedback from some of these discussions. That's quite something, actually. BTW, as to some of that "feedback": Honestly, I think the way you communicated your thought process and results was confusing for some people in the room. "Okay, pal - let's put your people up in a room full of strangers (some of whom show through their body language and/or constant phone-checking…

Without context the second quote sounded to me not like an accusation of slacking but like a warning that the company is bad place to work at (poor life-work balance)...

Completely agreed. An interaction like that during or after an interview would be a big red flag that it's not a place I want to work.

Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs

#92
post #60

The real reason you were rejected, or anyone applying in a highly competitive field: an oversupply of qualified candidates.

But that offers no explanation why some are hired and some aren't.

Because SOMEONE has to be hired?

Let's supposed, for a moment, that the hiring was completely random. 100 people apply, they pick a random name out of a hat, and hire that person.

Your name might never be chosen.

Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs

#94
post #58

Earlier quoted context omitted.

Not just blogging, but Tim is also active in terms of attending / speaking at various industry conferences and what-not. Whether he is doing it simply to "build his brand" or because he genuinely enjoys it, or both, is something I can't speak to. But I think he's definitely "meeting people and having interesting conversations".

Hi Phil!

Oh no, I've been outed!

Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs

#95

Many recommend setting up an online portfolio for these types of positions. But I've applied to a number of Data Analyst/Scientist jobs recently and I am immediately rejected almost every time despite highlighting my blog/portfolio ( http://minimaxir.com ) and my GitHub with open-source code/Notebooks for each and every post ( https://github.com/minimaxir ), both of which have topped HN on occasion. Internal recruite…

https://www.linkedin.com/in/minimaxir/

It turns out that QA was only the tip of the iceberg. You have a job title as "QA" and a job title as "support".

You will NEVER get any development or engineering job with that. Expect 90% reject in the resume screening because you are not a developer.

If they were development jobs, replace both by "software developer".

Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs

#96
post #57

Earlier quoted context omitted.

It's literally the first thing you learn in data science / machine learning coursework about evaluating model performance. It would probably be better to ask the candidate to whiteboard a set of metrics for evaluating model performance rather than ask for the definition of a pair of words, but the concept is practically the for-loop of data science. Edit: note that I'm not saying you need this to add roi as an analys…

I haven't taken a lot of data science classes but I'm not sure that's true. If you start with linear regression the mean squared error would make more sense. I actually searched through "The Elements of Statistical Learning" and the word 'recall' is not used in this sense at all.

The jargon does vary by subfield and community, along with the actual measures used (sometimes it's just a different name, but sometimes practices are different as well). Precision/recall are terms from information retrieval that migrated into the CS-flavored portion of machine learning, but are not as common in the stats-flavored portion of ML, in part because some statisticians consider them poor measures of classifier performance [1]. Hence they don't show up in the Hastie/Tibshirani/Friedman book you mention, which is written by three authors solidly on the stats side of ML. It does occasionally mention some equivalent terms, e.g. Ctrl+F'ing through a PDF, I see that in Chapter 9 it borrows the sensitivity/specificity metrics used in medical statistics, where sensitivity is a synonym for recall (but specificity is not the same thing as precision). It looks like the book more often uses ROC curves, though, which have their own adherents and detractors.

[1] This paper is the one that most often gets cited as background by people who don't like recall/precision as metrics: http://dspace2.flinders.edu.au/xmlui/bitstream/handle/2328/2...

Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs

#97

I manage a data science team and revamped the hiring process pretty substantially about a year ago, to good results. Nothing in here is particularly original, but here's what we do: 1. Break down "data science" into several different roles–in our case, Analyst (business-oriented), Scientist (stats-heavy), Engineer (software-heavy). Turns out that what we mostly want are Engineers-Analysts, so our process screens heav…

I've been looking for a job, and I've found how vaguely organizations define their data scientist and analyst roles in their job postings really frustrating. They tend to have a short description of the role, which is generally filled with buzzwords, followed by a list of requirements. I wish organizations would talk about what they wanted to do with their data instead.

For instance, a common description might say the candidate will be working with "big data" to help with "data-driven initiatives" and the requirements will be something like "knowledge of Excel, with a Masters in Statistics, or equivalent experience".

It's really hard to tailor a cover-letter or a resume to a job posting like that. For one thing, I can't even imagine what kind of work they are doing if they are using Excel for "big data". Second of all, I currently have a job, and writing cover letters and creating resumes takes a lot of time. By the time I get to the phone screen I've probably already spent at least a couple of hours applying. Plus, in the interest of keeping my cover letter and resume short, I have to leave off a fair amount of my experience and performance metrics.

Honestly, at this point I think I'm just going to start reaching out to people in the fields I'm interested in and asking them if they know of any roles that would fit my skill-set. The way I see it, I'd at least have a chance of getting feedback from someone who can view my skill-set holistically, rather than HR, who will let me know that I don't tick all their boxes (or vice versa).

Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs

#98
post #8

Companies often use interviews as a time to figure out what they're really looking for. For startups, this transcends data science. It might be the one time that week they focus on that need. Networking is still king. Exactly and this also argues against wanting to get hired to work remotely.

Not sure networking is contrary to working remotely. Opportunities for networking online are far bigger than in-person.

Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs

#99
post #48

I was coming from academia with no real competitive background in machine learning/predictive analytics/statistics when I started applying for data science positions. I was coming from a post doc in computational neuroscience, and had done ML/your run-of-the-academic-mill stats as part of my thesis, but never had formal training in it. I landed a lot of interviews (and applied for an ENORMOUS amount of positions), bu…

I still feel grateful for an otherwise-pretty-terrible employer for gifting me with a 'developer' title. Regardless of how much experience or aptitude one has or can demonstrate programming or building software, titles are crucial.

Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs

#100
post #51
post #29

At least you have been told the reasons, even if they are true or not. I recently had many tech/behavioral interview with team and passed, but after a review with VP of X, saying we decided not to move forward is way worse than this. I still keeep wondering 'What is wrong with me?' even after years of interviews. I have some suspicions, but never a promising answer. If I oneday found a company, first company policy w…

> first company policy would be to tell the candidates to tell why they were not hired in a polite way. And your lawyers would strongly advocate against that. In most cases they can't/won't tell you because it could open up legal liability. If they say a reason for you, but then hire someone else to whom that reason also applies and you find out, you could sue them. Or you could find a way to twist it into something…

This is part of why I dislike employment anti-discrimination laws. They probably worked better for manual labor jobs. Of course, there is also firing where the laws vary by state. I think CA is one of the most strict, right?
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