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
#2"Networking is still king"
My takeaway from it.
Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs
#3The real reason you were rejected, or anyone applying in a highly competitive field: an oversupply of qualified candidates.
Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs
#4The real reason you were rejected, or anyone applying in a highly competitive field: an oversupply of qualified candidates.
But, wait! I thought there was a "shortage of tech workers."
Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs
#5The real reason you were rejected, or anyone applying in a highly competitive field: an oversupply of qualified candidates.
In this particular case (data science) it is more an oversupply of candidates (qualified and not), plus difficulties defining and measuring "qualified", plus buzz.
It's a difficult enough field to hire in when you understand what it is (and isn't) - and lots of companies are trying to do it with far more vague goals.
Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs
#6The real reason you were rejected, or anyone applying in a highly competitive field: an oversupply of qualified candidates.
In this particular case (data science) it is more an oversupply of candidates (qualified and not), plus difficulties defining and measuring "qualified", plus buzz. It's a difficult enough field to hire in when you understand what it is (and isn't) - and lots of companies are trying to do it with far more vague goals.
Why do you think candidates for data science jobs aren't qualified relative to other positions?
Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs
#7"Networking is still king" My takeaway from it.
I think that just "networking" would be the wrong takeaway, though. The author[0] is pretty active on Twitter / the blogosphere and, even though I don't follow him personally, his writing has popped up on my feed a number of times. He's likely meeting people and having interesting conversations ("networking") because he has interesting things to say.
Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs
#8Companies 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.
Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs
#9Earlier quoted context omitted.
In this particular case (data science) it is more an oversupply of candidates (qualified and not), plus difficulties defining and measuring "qualified", plus buzz. It's a difficult enough field to hire in when you understand what it is (and isn't) - and lots of companies are trying to do it with far more vague goals.
Why do you think candidates for data science jobs aren't qualified relative to other positions?
I've probably interviewed about 70-100 such people in the past year and a half. Exactly 1 such person was qualified (I hired him). The issue in my view is the following: people who know both statistics and computer science are extremely rare.
People who actually understand statistics are rare. I can probably weed out 1/3 to 1/2 of candidates simply by asking what a p-value is, or what precision/recall are (this includes people who said they worked in search).
Of the ones who know basic stats, most are neither good at nor interested in programming. They just want to use existing libraries to crunch numbers in a Jupyter notebook, then hand that off to the developers.
Finding a person who can come up with a predictive model, understand what they did, optimize it without breaking it's statistical validity and deploy it to production is very hard.
(If you can do this, I'm hiring in Pune and Delhi. Email in my profile.)
Re: Some Reflections on Being Turned Down for a Lot of Data Science Jobs
#10This is a bit off-topic but one thing I'm curious is how the author manages to interview with these companies while holding down a full-time job and keep up-to-date with the latest developments. Interview for a data science position is typically a drawn-out process, with multiple rounds of interviews and possibly take-home projects. I found them to be very time and energy consuming.
To share my story, I also had a difficult time transitioning into a data scientist role after leaving academia (pure mathematics), and I always thought the root cause was my lack of experience and competency. So instead of keeping on applying, I spent over a year just to sharpen up my skills. It paid off in the end.
How can one develop his/her skills and cultivate expertise if one is job-shopping all the time (possibly aimlessly)?