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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

#131

Post by CryoLogic »

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.

I've seen this happen. Most companies cannot hire competent recruiters. Often recruiters have no technical background, and are unfamiliar with all but the buzzwords.

This trend will probably continue until someone decides to up recruiter pay and hire engineering background candidates for recruiting roles (if possible).

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

#132

Post by bbarn »

tom_b wrote: post #45
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…

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…

You're probably on to something there. Sadly, the opposite of this is true too - it's incredibly hard to find competent QA Engineers (not testers) because they all realize very quickly they can make more money as rank and file software devs than QA-side specialists, even if their role in QA is leagues more complex.

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

#133

Post by bbarn »

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…

HR Departments have also groomed hiring managers not to ever give a reason why they say no. Every reason you provide is grounds for speculation and potential law suits.

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

#134

Post by minimaxir »

dataframing wrote: post #108
NumberCruncher wrote: post #103

Earlier quoted context omitted.

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…

Thanks for the optimistic descriptions of my posts. :)

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

#135

Post by mulmen »

FeatureRush wrote: post #53
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…

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)...

I recently asked about the "unlimited vacation" policy and received a vague answer about "establishing expectations with your manager" and then was told they went with someone else.

I thought about if I should ask or not but I honestly don't understand how such a policy could be good for employees so I wanted an idea of the culture that allows it. If that question contributed to them not giving me an offer then good riddance so I don't regret asking.

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

#136

Post by micro_cam »

SatvikBeri wrote: post #119
walshemj wrote: post #116

Earlier quoted context omitted.

can I ask why functional programming in particular I can see why you might want to avoid java for big data - but isn't the average ML algo more in the procedural mould? Would not python with numpy be a better fit ? or fortran with some handwave interface code Back (early 80;s) when I did map reduce we used PL1/G

The most direct reason is because the current team enjoys functional programming. From a business standpoint though, there are a few main reasons: –Data pipelines are well modeled as functions: they take a few input datasets, return a few outputs at the end, and do a ton of processing in between –FP idioms generally make parallelization easier, and this is very important for the datasets we're dealing with –A strong…

Have you had performance issues getting things to conform to functional paradigms?

For example i've found that as a pipeline gets optimized for production use it needs to preallocate all of its output space and then modify things in at each step (like a one hot encoder flipping a few bits in specific rows of a zeroed array instead of allocating new ones and copying them in).

I find it difficult to reconcile this sort of code with a "pure functions without side effects" philosophy and still have it perform an an acceptable level.

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

#137

Post by SatvikBeri »

micro_cam wrote: post #136
SatvikBeri wrote: post #119

Earlier quoted context omitted.

The most direct reason is because the current team enjoys functional programming. From a business standpoint though, there are a few main reasons: –Data pipelines are well modeled as functions: they take a few input datasets, return a few outputs at the end, and do a ton of processing in between –FP idioms generally make parallelization easier, and this is very important for the datasets we're dealing with –A strong…

Have you had performance issues getting things to conform to functional paradigms? For example i've found that as a pipeline gets optimized for production use it needs to preallocate all of its output space and then modify things in at each step (like a one hot encoder flipping a few bits in specific rows of a zeroed array instead of allocating new ones and copying them in). I find it difficult to reconcile this sort…

We're mostly doing ETL on large datasets, so the code needs to parallelize well, but beyond that performance isn't really a big concern. We use ML in research, but no models in production, because the costs of increased maintenance/lost transparency generally outweigh the benefits in our use case.

In jobs that were heavy on ML, I would use high-performance tools for the models (imperative code, numeric computing packages etc.) and functional code for the ETL, which worked pretty well–no need to be dogmatic about it, a 70% pure codebase is still generally easier to reason about than a 20% pure codebase.

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

#138

Post by AstralStorm »

dandermotj wrote: post #77
halflings wrote: post #12

Earlier quoted context omitted.

Ignoring what a p-value is does not mean that you don't know statistics. p-tests are not some inherent statistical property, they're just a useful model for significance. People coming from a CS background most likely didn't have to deal with p-values, but they can still be good at linear algebra or bayesian statistics. (not sure I can defend somebody that does not know what precision/recall are)

You're certainly not going to study Bayesian statistics without knowing (or at least having studied) what a p-value is.

Actually p-values are used way less often in Bayesian statistics than frequentist ones. The latter rely on statistical tests more.

Bayesian stats tend to use likelihood ratios or Bayes factors instead of p-values for hypothesis testing.

The trick in all cases is that you're comparing to expected results given some prior distribution. Most people use a dumb prior (e.g. Gaussian) and then they're confused when the numbers make no sense as data is multimodal or heavy tailed, thus mismodelled.

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

#139

Post by PakG1 »

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…

Yes, you're correct. But it's best to walk away from companies who try to own you to that level. If a good company respects and wants you, they are completely OK with giving up some of their leverage in order to gain your trust as well as they can.

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

#140

Post by hudibras »

bsg75 wrote: post #55

> 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. Dodged a bullet on that one: - PTO is a touchy subject? - They are looking at more than one candidate, why would any candidate limit themselves to one potential employer?

Yeah, that's just weird. Paid time off is part of the total compensation package; it would be strange if a candidate didn't ask about it.
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