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

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

#101

Post by thomasthomas »

yttrium wrote: post #49
tom_b wrote: post #45

Earlier quoted context omitted.

I wonder if you are less impacted by the lack of CS degree than by your "Software QA Engineer" label. My own experience was that my initial position as a software performance engineer resulted in a perception that I was a "tester" without technical skills despite having multiple CS credentials and published code in practitioner-oriented sources. Overcoming recruiter biases was such a struggle that I now routinely cou…

I had a similar experience. My first job out of college was for a Developer Role (building testing frameworks, maintaining and building browser extensions) but the job was titled 'QA Developer' so I had a hell of a time the first time I tried to find a new job. Never mind that I wrote thousands and thousands of lines of application code, lots of recruiters would deny me on the basis that my background didn't fit.

Some companies are really dumb and only consider candidates with specific job titles on their resume. I've had recruiters from contract companies say "your experience is great but can we change your previous job title to X." Recruiter knows the client gets hung on job titles if its not exactly what they're looking for. In my experience i'll change previous job titles to fit the position i'm applying for, as most of my previous jobs I could've been titled 4-5 things.

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

#102

Post by minimaxir »

user5994461 wrote: post #95
minimaxir wrote: post #22

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".

Those were my college internships, as you can see by the dates. (and obviously I do not include them in my resume)

My most recent job is obfuscated a bit in the public eye/only visible to logged-in LinkedIn users, for good reason. (I politely request people looking into it not discuss it)

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

#103

Post by NumberCruncher »

minimaxir wrote: post #22

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…

Your portfolio sais "I can plot public data in colour". It should say "I understand and can apply in practice a couple multivariate modelling techniques". Learn until you understand why we don't blog about single decision trees.

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

#104

Post by user5994461 »

minimaxir wrote: post #102
user5994461 wrote: post #95

Earlier quoted context omitted.

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".

Those were my college internships, as you can see by the dates. (and obviously I do not include them in my resume) My most recent job is obfuscated a bit in the public eye/only visible to logged-in LinkedIn users, for good reason. (I politely request people looking into it not discuss it)

> and obviously I do not include them in my resume

You remove the experiences you have and wonder why your resume doesn't pass a screening test? Must seem quite empty.

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

#105

Post by CalChris »

emplynx wrote: post #98
CalChris wrote: 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.

You may be talking about contracting and the OP is talking about hiring. Moreover while the opportunities for networking online are countably infinite they can be thin opportunities. I know Joe from HN doesn't carry the same weight as I know Roger from Google. It carries weight just not nearly as much.

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

#106

Post by alain94040 »

You never really know

That's actually the only correct answer. Having been on both sides of the table for many years, I can pretty much guarantee that whatever reason the candidate is given is nowhere close to the actual reasons.

There may not be any specific reason why we didn't pick you, but we'll give you a tiny sample anyway. So you think that's the reason - it's not.

In other cases, we have a strong reason not to pick you, but it's embarrassing so we feed you a bogus reason instead.

And in more cases than I'd like, there is no problem on your side, we are having internal issues that we can't reveal anyway. Any reason you hear from us in that case is completely irrelevant.

By the way, when you evaluate people in interview, you really need to figure out a vector: where they are today in terms of knowledge and experience, but also in what direction they are going (fast learner or not, high potential, etc.). Which is why sometimes you can hire someone who has less relevant experience, but you think they'll learn fast and are very smart, and sometimes you are looking for the perfect match to the current position, but don't care too much whether they can pick up new stuff or not.

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

#107

Post by [deleted] »

alain94040 wrote: post #106

You never really know That's actually the only correct answer. Having been on both sides of the table for many years, I can pretty much guarantee that whatever reason the candidate is given is nowhere close to the actual reasons. There may not be any specific reason why we didn't pick you, but we'll give you a tiny sample anyway. So you think that's the reason - it's not. In other cases, we have a strong reason not t…

[deleted]

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

#108

Post by dataframing »

NumberCruncher wrote: post #103
minimaxir wrote: post #22

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…

Your portfolio sais "I can plot public data in colour". It should say "I understand and can apply in practice a couple multivariate modelling techniques". Learn until you understand why we don't blog about single decision trees.

I have to disagree. The blog is very heavy on the data visualization -- but is that a bad thing? The posts effectively cover most of the data analysis timeline (collection, iterative exploration, model building, visualization) and they're well-explained and thoroughly-explored.

Others have said the same, but I'd wager the biggest issue is (a) the limited past roles that directly dealt with data science/analysis or (b) SF's narrow-minded hiring practices. Maybe a masters or data science bootcamp would go further than more blog posts?

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

#109

Post by nickdavidhaynes »

SatvikBeri wrote: post #35

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…

Could you talk more about your phone screens? What criteria are you filtering on? What sorts of questions have you found to be effective at stratifying candidates?

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

#110

Post by antognini »

kenjackson wrote: post #43
Bootvis wrote: post #34

Earlier quoted context omitted.

Regarding precision/recall, I've a background in financial econometrics and this is the first time I encounter the terms.

I think the problem is that certain subfields use different terminology to mean similar or identical concepts. For example, while I'm in software, I tend to hear the terms sensitivity and specificity. They are historically medical terms. They aren't identical to recall/precision, but I think you can derive one set from the other.

The fundamental thing to know is the confusion matrix. There are about a dozen terms for various descriptors of the matrix, but they all can be calculated if you know the confusion matrix. The Wikipedia page has a great table to describe them all:

https://en.wikipedia.org/wiki/Confusion_matrix

You can see from that that sensitivity and recall are the same thing, but specificity and precision are not.

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