Some Reflections on Being Turned Down for a Lot of Data Science Jobs • Hacker News Board

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

#151

Post by preordained » Tue, Mar 07, 2017, 11:59 AM UTC

The too many jobs knock I can understand...I'm just plain old Midwest developer guy, so maybe I don't understand the nuances of a market like Silicon Valley or the like that is inundated with software jobs. Still, when I read HN I often get the impression that there is more focus on getting that next great offer than actually doing a given job...so much so that the ability to be perceived as valuable at interview/screening time seems like the mostly highly focused on or developed skill for some people. It seems like there are many professional interviewees out there these days.

I have a hard time understanding. At least for what I've seen/done, it would take about a year of experience to be of any real value. I'd say you start seeing real dividends from an employee near the three year mark. I think the primary exception would be where you have a big gaping hole in an organization...like building a data science program or something from the ground up. But if you've got software and customers long long past the 1.0 stage to support, and someone is going to (someday) understand/contribute to the core? Think about Google's monolith or the Linux kernel...sure most software isn't that mature/grand, but there are many projects out there that are closer to that than greenfield. And it's not just the code, it's the developed relationships/rhythm with coworkers and customers.

Maybe I've explained it to myself, I don't know. The difference may be startup companies or projects versus mature ones. At least in the latter case, it seems to me that if a company retains an engineer for less than 2-3 years, they've almost certainly lost on that investment.

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

#152

Post by autokad » Tue, Mar 07, 2017, 12:43 PM UTC

I can't even get a call back for an interview lined up. After hearing people get tons of unsolicited mails on linkedin from recruiters (while I got none), I tried adding more words in my linkedin to show up on searches, but still nothing.

Does anyone have any suggestions? Here is my linkedin account: https://www.linkedin.com/in/karl-dailey-02557b65

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

#153

Post by maus42 » Tue, Mar 07, 2017, 12:53 PM UTC

dandermotj wrote: post #150

AstralStorm wrote: post #138

Earlier quoted context omitted.

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 numb…

I studied statistics - my point was that statistics is taught in a linear manner, starting with distributions and hypothesis testing (p-values) and then move onto more advanced treatments like Bayesian stats.

That happens in statistics programs. However, I have a ML-heavy minor in CS, and based on the ML course contents at our CS dept I've seen, I'm not sure if the all their CS majors go through the the full canonical statistics curriculum, nor that they were intended to. At least the ML courses had quite much introductory probability and statistics as far as ML applications were concerned, so I understood the implication was they didn't assume that the students would have already done the similar stuff in statistics (though it certainly helped), and I can't remember a single mention of p-value there.

And then there's this, that even if your intro to probability course everywhere covers the classic statistics with p-values and hypothesis testing and frequentist confidence intervals and so on, you are not necessarily going to use them that much. I calculated some p-values and other tests with R for some example datasets a couple of years ago and never seen them since in coursework, everything we've done after that has been more or less fully Bayesian. The concepts are still fresh[1] in my mind mostly because I read some statistics blogs, such as Andrew Gelman's [2]. The irony is that Gelman does not exactly love frequentist framework, he just mentions its concepts often enough.

[1] or not totally forgotten

[2] http://andrewgelman.com/

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

#154

Post by kod » Tue, Mar 07, 2017, 1:35 PM UTC

cjhanks wrote: post #118

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

Functional programming for a lot of numerical computing maps easier to mathematical notation. However, Scala is usually a worse choice than Java for numerical computing since everything is a boxed type.

This is straight up false, why do you think Scala doesn't have primitive values? Long will be either a value or reference type as needed, despite being spelled only one way instead of two different ways in java.

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

#155

Post by autokad » Tue, Mar 07, 2017, 3:52 PM UTC

apohn wrote: post #129

Declanomous wrote: post #97

Earlier quoted context omitted.

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 th…

>>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. I lead a Data Science team and part of the struggle with writing sensible job descriptions is that there are too many people providing input into the job description. HR can also put their hand in the pot when they try to use buzzwords (e.g. Hadooop) to intern…

how do you choose candidates for an interview?

I can't even get a call back for an interview lined up. I have done NLP, got a masters degree in computer science from Penn, plenty of experience with big data such as hdfs and hive, spend my free time doing what ever data science I can. but obviously doing something wrong.

any suggestions? Here is my linkedin account: https://www.linkedin.com/in/karl-dailey-02557b65

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

#156

Post by leblancfg » Tue, Mar 07, 2017, 4:22 PM UTC

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…

Just to play devil's advocate here, in many cases the reasons stated for refusal are only one part of it.

In his case, the author doesn't seem to have had an offer on the table. Perhaps the interviewer felt as though the candidate was trying to negotiate too much, too early in the process. Perhaps the interview didn't go over all that well?

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

#157

Post by cema » Tue, Mar 07, 2017, 4:54 PM UTC

minimaxir wrote: post #82

melvinmt wrote: post #50

Earlier quoted context omitted.

> 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.

Maybe you could rephrase it. For example, give it a title "software engineer", and then a subtitle or one short paragraph explaining: "the position was officially called qa whatever and involved developing software for this and that", and then list the accomplishments.

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

#158

Post by LeanderK » Tue, Mar 07, 2017, 5:28 PM UTC

Well, it also seems like he applied to a lot of jobs (he worked for 5 companies since 2012, for many only under a year!). He must be constantly looking for a new job.

I know switching jobs is common here, but i would think that sticking at least 1 to 2 for a job would be normal (assuming it works out).

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

#159

Post by kafkaesq » Tue, Mar 07, 2017, 5:30 PM UTC

mulmen wrote: post #135

FeatureRush wrote: post #53

Earlier quoted context omitted.

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…

No need to keep asking. If your get is telling you that something's not quite with this "unlimited vacation" policy -- it's because it's a sham, basically.

And everyone at the company knows it -- including and most especially, upper management. Because that's after all where it originated.

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

#160

Post by eanzenberg » Tue, Mar 07, 2017, 6:14 PM UTC

FireBeyond wrote: post #123

eanzenberg wrote: post #15

My analytical thought process on DS interviews: - Signal is still quite low among noise, even with long multiple interviews, take-home homework, coding challenges, etc. Most relevant data is still hidden and takes months-years to come out. - Companies seek to minimize false-positives much more than minimizing true-negatives. - It's a numbers game from both ends because the probabilities are low, due to above 2 points…

Woe betide the company that asks for a take home project, especially one that isn't just 50 lines of an algorithm (I'm talking one that I recall that asked for parsing Apache logs from a stream, displaying moving averages for URLs, aggregates, having high water mark "alerts" and resetting when rolling averages dropped below that, some unit tests and docs... And doesn't bother to respond after you submit. I'm certainl…

Same FireBeyond. Even worse, I've gotten feedback on a take-home project where the solutions had errors in it and I had the correct response! This, btw, was from one of the huge tech-companies in SF. Since it was the recruiter who went through the solutions, I couldn't nor cared to correct their mistakes :)