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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

#81

Post by SatvikBeri »

dwpdwpdwpdwpdwp wrote: post #20
yummyfajitas wrote: post #9

Earlier quoted context omitted.

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 inclu…

I'm surprised people bother spending so much energy looking for someone who is both a statistician and a computer scientist knowing they are so rare. There are so many more statisticians who can at least communicate and work effectively with developers and vice versa. Why not just compose a team? I feel like just like other professionals have assistants, statisticians should have them too, and they'd be focused on th…

This is a good question. I've tried both approaches, and currently favor going after the rare multi-skilled hire. In general, I have seen many cases where one person who has a small-medium amount of experience/ability in both is a lot more productive than two specialists.

> There are so many more statisticians who can at least communicate and work effectively with developers and vice versa.

Not in my experience. You need to design your data infrastructure to promote easy analysis, and you need to design your models to scale well according to the amount of data you're working with. There are also many cases where a project will require mostly engineering work for a while, and then mostly analysis/statistics work–there are ways to handle this with specialists of course, but there's generally a significant switching cost.

Also people with a combination of statistics & programming aren't that rare–IMO it's more that employers tend to search for both degrees, when instead you should be trying to evaluate the skills directly.

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

#82

Post by minimaxir »

melvinmt wrote: post #50
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…

> my Software QA Engineer background + no CS degree implies I have no technical skill It's OK to leave stuff out on your resume if it's not relevant (and maybe even harmful) to the position you're applying for.

My Software QA Engineer position is my first, and only job post-undergrad.

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

#83

Post by user5994461 »

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.

Replace by Software Engineer. Problem solved!

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

#84

Post by pbhjpbhj »

dwpdwpdwpdwpdwp wrote: post #41
ska wrote: post #27

Earlier quoted context omitted.

This is a classic problem that shows up equally with lots of related areas: numerical work, statistics, ML, signal processing etc. "just compose a team" sounds easy, doesn't it? Unfortunately there are lots of failure modes involving different parts of the team not really understanding what each other are trying to do, let alone what they are doing, and subtle errors getting by people who don't know what to look for.…

As an engineer, I usually know better than to use the phrase I hate to hear: "why don't you just..."

Surely "Why don't you just... ?" is an exceptionally good phrase to use. In practice people mean "Just do ... !", which is very different. The why question, however, gets to the heart of an issue, it's a short hand for "The obvious solution appears to be that ... but I imagine you tried that and have a reason not to do things that way, what are those reasons?". It's a direct learning-centred enquiry that ekes out the kernel of complexity of a situation relying on the wisdom of the person it's aimed at.

So why don't you just use the phrase "Why don't you just ...?"?

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

#85

Post by [deleted] »

user5994461 wrote: post #32
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…

The first job is the hardest to get. Keep trying, lower your expectations: Take anything you can and don't expect to be paid much. Contrary to what the reddit folks would have you think. The ONLY thing that consistently get someone through the door is demonstrable real work experience, on real projects, in the industry (read: not academia). Side projects are not a substitute and the lack of degree is a barrier.

[deleted]

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

#86

Post by daenney »

namelezz wrote: post #71
kafkaesq wrote: post #47

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…

> Honestly, I think the way you communicated your thought process and results was confusing for some people in the room. Wow, get similar rejection lately: this is a fast paced company and the way you explained your thought process was confusing and might slow us down, so good luck with your job search.

Did you ask them what was confusing and how it would slow them down. Did they tell you? Because ultimately if I'd hear that without anything to help me improve it's a bit of a catch-all "we don't know so out you go" kind of thing.

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

#87

Post by strictnein »

BickNowstrom wrote: post #80
strictnein wrote: post #37

> "Quite honestly given your questions [about vacation policy] and the fact that you are considering other options, [we] may not be the best choice for you." I had a very similar experience. Job offer was basically on the table and then they balked because I mentioned that I had another offer (at a larger company, which they seemed shocked/annoyed with) and I had a question about parking at their new offices. The cur…

Mentioning that you had another offer can be tricky. Did they ask you if you were seeing other companies? Did you just put it out there? Did you put them on a deadline? Mentioning that you have another offer is seen as a negotiating tactic. Then they have to compete/bid against the other offer, and you are removing some of their leverage/power. A strong reaction to such a move can be to cut off the interview, so to r…

I think they may have asked me, although, to be honest, I'm not 100% how it came up. It wasn't me putting them on a deadline though, I just try to be pretty open about things in general.

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

#88

Post by strictnein »

komali2 wrote: post #78
strictnein wrote: post #37

> "Quite honestly given your questions [about vacation policy] and the fact that you are considering other options, [we] may not be the best choice for you." I had a very similar experience. Job offer was basically on the table and then they balked because I mentioned that I had another offer (at a larger company, which they seemed shocked/annoyed with) and I had a question about parking at their new offices. The cur…

Both of your points I find extremely surprising. I've found that mentioning that I have other offers makes me a more competitive pick (this reflects my experience when I was a recruiter as well), and that's very weird that they balked at your question about parking. Honestly you probably dodged a bullet.

That's been my takeaway too. Company was purchased by a huge private equity firm last fall (KKR), so I probably would have been out of there by now regardless.

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

#89

Post by achompas »

jknoepfler wrote: post #52
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.

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…

For those downvoting this comment -- it's absolutely true that model performance is discussed at length early in ML courses (usually in the context of the bias-variance tradeoff).

My only quibble would be that precision + recall are one set of evaluation metrics applicable to classification tasks. Modelers can absolutely use other loss functions.

Additionally, precision/recall do not map nicely to regression problems, so people use other metrics (RMSE, MAE, etc.).

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

#90

Post by [deleted] »

jknoepfler wrote: post #52
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.

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…

[deleted]
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