If they are getting much less than one applicant per job it's clear they are doing something wrong.
Jobs get spam applications and it might be that anything from
50-90% don't get a second glance; if you are a fit for the job that won't be you, your odds will be much higher.
It's a lot like posting response rate. For one thing, it's a polite rejection. Politeness encourages more applicants. Not having a black hole will also encourage applicants to complete the process. There's also some fatigue/anxiety as part of a long process, which will affect the requested salary or acceptance - someone is more likely to accept a well paid job with a pleasant process than a well paid job with a tiresome process.
YC, Toptal, and many universities post their acceptance rate, and it's only proven a good thing. The ambitious ones will be encouraged to try even with a low acceptance rate.
Google Software Engineer I - 100 total jobs, 2000 applicants (click here for a breakout by region). Based on historical data and the current applicant pool, you have a 8% chance of scoring an interview if you apply to [All Locations] and 5% if you apply to Mountain View only.
Aerojet Rocketdyne Inc. Software Engineer I - 10 total jobs, 150 applicants (Sacramento only). Based on historical data and the current applicant pool, you have a 20% chance of scoring an interview.
I know that withholding such statistics is advantageous for the employer and maximizes the number of applications. However, my question is directed more at job platforms like LinkedIn. At most, they only show the number of applications. When I apply, I don't have the faintest idea whether I'll get a response or whether my application will go into a black hole. I believe a more applicant-friendly job platform, which publishes more statistics, aggressively penalizes outdated/lame listings, and incentivizes updates from the employer will attract more applicants. Those applicants will in turn attract more companies, so on and so forth.
The job search grind is a major unsolved problem. Unless you have the most fungible skillset and level of experience, applying to jobs is soul crushing. We don't even have read receipts for resumes. There are so many low hanging fruit which haven't been touched by the major job platforms. People are not opposed to long job applications. What is deeply problematic is that there doesn't seem to be a correlation between the length/redundancy of a job application and your chance of getting a callback. With ML, it doesn't seem impossible for LinkedIn to modify its platform such that it can give you reasonable assurance as to your chance of getting a callback. Solving this issue will be nothing more of revolutionary. It would single-handedly make the economy more dynamic. I'd love to hear an informed take on this issue though, maybe I'm missing a key constraint.
I agree, that's a great approach given the status quo. However, I'm talking about how things could be rather than how things are. The method you outlined is profoundly inefficient. I also highly doubt there is empirical evidence which shows that leveling an implicit "networking barrier to entry" is superior to alternate methods of hiring. It makes our labor market much less dynamic.
> The method you outlined is profoundly inefficient.
It's based on a time proven Sales Prospecting formula. Used especially when determining Product/Market fit.
NOTHING will happen until someone reaches out and expresses an interest.
Understand you have two very different dynamics at play.
A) Companies That Need Talent
B) Talent That Need/Want Work
The expectation that employers will accurately articulate what they want-- when they want it; has a basic human flaw.
Even seasoned executives have trouble expressing what they truly WANT.
Similar problem for Job Seekers, who often have trouble positioning their own strengths and abilities on paper.
Efficiency success models are hard to find in this space -
Google famously built a top-tier employer brand. Yet people who have lived through their application/interview and hiring process describe it as dreadful. Humans still drive the hiring engine.
There is zero correlation between unstructured interview performance and on the job performance. You read that right, zero. Interviewers would like to think they can discriminate talent based on interview performance. On average, they don’t add one iota of value. Collectively, they are wasting millions of hours a year and destroying untold billions of dollars in economic value.
You might have realized that I added the qualifying adjective “unstructured” to the claim about interviews (there is plenty of empirical literature). Structured interviews are more predictive. However, some of the function of a structured interview can be replicated at third party test centers which give you a score. Employers can select for the best and most predictive tests in the ecosystem. Furthermore, those employers who don’t know exactly what they want are the least likely to have structured interviews. They are probably taking the unstructured route, and kidding themselves into thinking that they’re selecting for talent during the interview process. In reality, the best they are doing is soliciting a pool of candidates from good schools and prestigious companies. Don’t get me wrong, I’m not saying get rid of interviews. However, most interviews should be structured, and companies should outsource most of the process so that:
- they can interview more candidates
- and more of those candidates would be a good fit going in
At the level of people like Tim Armstrong, interviews are just a formality. Their track record is so self-evident that the best candidate out of an already excellent set of candidates might be obvious. Even if the best candidate isn't obvious, any of the other pre-filtered candidates is almost as likely to excel. This is not all too different from unstructured interviews at lower levels. Pre-filtering based on high-value signals is what really matters at the end of the day. It's a waste of time to pretend that unstructured interviews or "playing the game" and networking creates more value than it destroys (on average).
I agree that that’s how to deal with the status quo, but my premise is that the status quo is inefficient and there is a lot of value to be created by changing it. Everyone should indeed network, but networking shouldn’t play as outsized of a role in the hiring process as it does today.
I recently spoke with someone who networked and got a great job at a top MBB consulting firm, but she was days away from giving up and settling for a job in a non-target industry that pays half. She tried to network with less highly ranked consulting firms and it didn’t work out. How arbitrary. Her projected net worth jumped by millions in the span of days, with no underlying value creation. The whole process really is an unmitigated disaster.
The gatekeepers are there for a reason. Unless you already know someone who sits high in a company, this won't work. Every Bozo jobseeker does this on LinkedIn too, and they're usually worse than the ones who follow the process.
Do you know empirically that that’s the ideal way to screen candidates? Because that would be the only good reason for so many companies to hire in that manner. There are many great engineers who, precisely because of the traits that make them great, would “fail” that Kobayashi Maru test and settle for a suboptimal role. Not only do they lose out on experience befitting of their aptitude, but the company loses out on the best candidate, and the economy loses out on the surplus created by ideal labor matching.
The job search process, as it stands, is another high entropy game in a life full of games. Many companies have reached the heights of success by reducing or eliminating one of those games. Ebay is one example - it takes out all the posturing and dumb games involved in selling an illiquid item. As a result of Ebay, many more of those items are sold, for higher prices, more quickly, and to the people that ascribe the highest value to them.
Sure, the job search "game" cannot be completely eliminated. There will always be some fuzziness. However, the fuzziness can be greatly reduced on both sides, and that would be immensely valuable. You have way more faith in the value of the current job search game than I do.
I think it's mostly because the statistics don't tell the story. I've been on the hiring side for 3 companies. We'd take the first non-idiotic person who applies.
If you're the kind of person to be a friend, you'd likely be hired. If you're the kind of stranger looking for a job, you'd be unlikely to be hired. If you're the kind of person to hang out on HN or some Facebook/Slack meme groups, we couldn't afford your salary.
Out of that, it would be roughly 90% of applicants who don't qualify, 5% who we can't afford, and somewhere in between which we just settle for. Many new graduates can't do FizzBuzz, many people who apply for a senior position can't reverse a linked list.
“We'd take the first non-idiotic person who applies.”
I think it would still help for a company like that to display stats. For example, if you’re a qualified candidate who is applying early, you’d see an abnormally high probability of callback. If you’re an unqualified candidate, you’d see a very low probability of callback. I mentioned that ML can help calculate these probabilities. In addition, I think it might help for companies to secretly encode “hard” job requirements. Most requirements are in fact soft, but the applicant never knows which is which. If they don’t meet a hard requirement, they should see a 0% probability of callback. Full stop. ML would do a good job of assigning importance weights to each of the requirements which aren’t encoded as “hard,” but it would start from weights provided by the employer when there isn’t enough data.
But stats like that also can signal being over-selective.
I would gently suggest that it is a mistake to think in terms of unqualified vs highly-qualified candidates and ML for "callback probability". I don't want a good, but not FANG-level, candidate to NOT apply for our standard software engineer opening just because 74 people already did.
Why? We had 60+ candidates for a recent posting for a junior developer role with some experience - think 1-2 years or some good school projects. Somewhere around 45-50 of those applicants were easily HARD-FLAGGED don't interview at all after a quick cover-letter and resume review by a technical person using a rubric. That rubric was scoped only to weed out applicants with absolutely no meaningful experience.
While I also hate the laundry-list approach for job listings (Must Have: Expert Java and C#, Spring ORM and ActiveRecord skills with deep understanding of React internals), I think the best way to attack that is with a short, but focused cover letter. That should include a short paragraph or two about a candidate's hands-on experience and speculate how their experience might apply to the position. I guess what I am driving at is that this idea of ML assigning importance weights to "soft" vs "hard" requirements is, at best, just another low-value signal in job postings that are already full of low-value signal.
Just like on your LinkedIn profile, there would be way more than a simple list of keywords and skills. The keywords would be part of a full profile which includes universities/degrees, certificates, years of experience, accomplishments, etc...some of which are easier for an ML to train on than others, but my point is those are stronger signals than “Java: Yes.” If there is residual fuzziness in the process, the platform can add skill tests (taken at a test center) that can get listed on your profile.
A third party ecosystem of skill tests could emerge, some of which would be more prestigious/respectable than others. Some would have a binary output, and some would give you a score that is benchmarked against the population of test takers. This would only really be necessary for more technical skills. I’m not talking about rinky dinky khaki cubicle test centers. I’m talking about new age corporations with $1B market cap that develop an unparalleled ability to assess skills so that corporate HR departments don’t have to. These testing companies would be part of the ecosystem developed by the theoretical ideal, statistically-oriented, applicant friendly job platform that I’m talking about.
Well, the thing is that interview calls are based almost entirely on resume and cover letter quality, not so much the skills. A hard requirement is that they should understand how case-sensitive and punctuation works, and many applicants can't even get their name in proper Name Case. Feedback is, well, if you school for 11 years and still can't use grammar where it matters, I'm not sure it helps. But too many do this.
We did reject one smart guy for an Angular post, because his only technical skill was WordPress and the gap for that is just too big. But we interviewed people with experience with only PHP and no JS. Skills are learnable.
There's also weird stuff like rejecting people because their expected salary is too low. It's a pattern that those with low salary expectations performed worse technically than juniors.
0% callback is also rare because of that. I got my first, web dev job, with no experience in anything other than HTML and an EE degree. Rejection rates for a role are 100% until the first hire.
In the group between "don't qualify" and "can't afford", we have a hard time with candidates who are unable (or maybe lack the communication skills) to effectively talk about how their experience+potential are a good match for even a generic job opening.
We don't do any algorithmic/challenge screening during interviews.
A common interview fail for us is to have a candidate that can't generalize from or build on something from his/her resume. This usually comes out during "tell us about project X that you worked on" and comments from our side like "oh, that sounds like project Y here. But Y is different this way - what are your thoughts about how to handle <insert small-scoped, specific difference>?"
Stats would be displayed prior to getting any interviews. If someone gets an interview and then fails at it, that’s on them. Stats can still help with baseline interview performance though. Machine learning algorithms can find a correlation between experience (or lack thereof) and interview failure. In response, the algos will encourage those candidates to apply somewhere where they have a better chance of getting in. However, those candidates can supplement their skill set. They can take tangible steps to increase their likelihood of getting the job.
Let’s say the applicant is a senior engineer but you’re afraid they aren’t keeping up with the latest frameworks. And let’s say that the employer (or the ML also) assigned high importance weights to those frameworks. Here’s what you’d see happen:
- The senior engineer with 15 years of experience at several BigCos but doesn’t have experience in that framework will see a low probability of callback
- The senior engineer with 10 years of experience and only one BigCo who’s skilled with that framework (the right keywords, and 2 MIT edX certificates in it) will see a high probability of callback.
What’s exciting is that this will encourage people to acquire exactly the skills that will help them get the job that they want. The applicants would be able to do scenario analysis on the job platform and see how much better their chances would be if they acquire a certain skill set. This would allow them to tune out the noise and focus on what it takes to get the type of job they want. Of course, the more senior the applicant is, the more accomplishments at past jobs matter. Even then though, senior applicants usually only display a fraction of their past experience. The platform can show their callback probability change in real time if they add a meaningful past experience or accomplishment.
The possibilities are endless. Are you considering doing a XY boot camp because you think it might get you into Z Co? Think again, with your experience + that boot camp, you will have a 2% chance of getting into Z Co and 5% chance of getting a similar role with similar pay. That alone would improve the educational and vocational pipeline.
The response is enthusiastic, but kind of misses the point my original comment was making. All I was saying in that comment is that as a hiring organization, we bring in candidates who on paper have the skills and experience we are looking for, but flame out in spectacular fashion when asked to talk about their skills, experience, and how they might apply to our specific problems and situation.
Note that I am carefully NOT saying, oh the candidate knows javascript, but not vue.js, so they don't fit for us. I am also not saying "oh, if only candidates knew we wanted them to get vue.js certified and it would be more likely we would put them in our interview loop."
I don't think the problem of how candidates can better understand why they are or are not getting callbacks from employers maps very well to any stats-based measurement of job posting descriptions against applicant resumes/CV/LinkedIn profiles.
My only thoughts on the hiring process are really anecdotal, but I believe a few things to be true.
1 - Most job descriptions are crap.
2 - Most jobs for software engineers can be done by any
reasonably trained and/or experienced candidate
who can pass a work-sample test.
3 - Totally untrained and inexperienced candidates
seem to apply to lots of jobs.
4 - Software engineer hiring should be ideally
based on work-sample tests.
4a - I have a *very* hard time finding companies
that do #4.
Sort of off-topic, but I'm interested in how often you reverse a linked list at your job.
Is that something that you expect people to be able to do? If so, what does it tell you about them? If they don't know what a linked list is or how to reverse it, what does that tell you about their real-world coding knowledge or lack thereof?
For anyone downvoting, it's a serious question. We're debating these kinds of algorithms as interview questions at my job, and I was curious what this brings to the table versus, say, building a CRUD API or constructing SQL queries or anything else.
For example, I've been in the industry for 15 years and I've never had to construct a linked list (much less reverse one) in my career. I have had to build APIs, construct DB queries, create distributed systems, etc, etc. So I naturally wonder why constructing a linked list might result in rejection for a job, unless it's something you're actually expected to do on the job for that role.
We don't use linked lists, but it's a test to see if they understand pointers. Pointers are used quite often for a number of things. A binary search might be a better test, but it's even harder and not as guided as a linked list modification test.
Oh, to note, it's a code modification test, not a whiteboard algorithm thing.
Ah, gotcha. That makes a lot more sense. If you were asking them to write a linked list implementation from scratch, I'd have a lot more questions. Having an already-implemented LL and modifying that is a lot more reasonable.
Sure, but LinkedIn doesn't always show you the true applicant-to-job ratio. For many great jobs at large companies, especially at lower levels, there are many "seats." LinkedIn doesn't tell you how many Software Engineer I's Google is hiring.
Moreover, I think LinkedIn could do a better job of presenting other statistics to job candidates and reducing noise on the platform. They have done little to mitigate the feeling that your resume is most likely to disappear into a black hole. When I apply to a job, I want a good approximation of how likely it is that I even get a response. As it stands, companies have zero incentive not to waste your time, and that is actually bad for the both of you. There is more discussion on this below.
32 comments
[ 98.0 ms ] story [ 2375 ms ] threadIf they are getting much less than one applicant per job it's clear they are doing something wrong.
Jobs get spam applications and it might be that anything from 50-90% don't get a second glance; if you are a fit for the job that won't be you, your odds will be much higher.
YC, Toptal, and many universities post their acceptance rate, and it's only proven a good thing. The ambitious ones will be encouraged to try even with a low acceptance rate.
Google Software Engineer I - 100 total jobs, 2000 applicants (click here for a breakout by region). Based on historical data and the current applicant pool, you have a 8% chance of scoring an interview if you apply to [All Locations] and 5% if you apply to Mountain View only.
Aerojet Rocketdyne Inc. Software Engineer I - 10 total jobs, 150 applicants (Sacramento only). Based on historical data and the current applicant pool, you have a 20% chance of scoring an interview.
I know that withholding such statistics is advantageous for the employer and maximizes the number of applications. However, my question is directed more at job platforms like LinkedIn. At most, they only show the number of applications. When I apply, I don't have the faintest idea whether I'll get a response or whether my application will go into a black hole. I believe a more applicant-friendly job platform, which publishes more statistics, aggressively penalizes outdated/lame listings, and incentivizes updates from the employer will attract more applicants. Those applicants will in turn attract more companies, so on and so forth.
The job search grind is a major unsolved problem. Unless you have the most fungible skillset and level of experience, applying to jobs is soul crushing. We don't even have read receipts for resumes. There are so many low hanging fruit which haven't been touched by the major job platforms. People are not opposed to long job applications. What is deeply problematic is that there doesn't seem to be a correlation between the length/redundancy of a job application and your chance of getting a callback. With ML, it doesn't seem impossible for LinkedIn to modify its platform such that it can give you reasonable assurance as to your chance of getting a callback. Solving this issue will be nothing more of revolutionary. It would single-handedly make the economy more dynamic. I'd love to hear an informed take on this issue though, maybe I'm missing a key constraint.
STOP applying to positions immediately.
Every Bozo jobseeker is applying to those posts.
And worse still, the candidates are sorted and selected by Flunky HR types.
Instead, consider your target audience.
ASK: Who is the guy/gal you can most help? What are their title(s)?
Think - CTO/CIO, VP or Director of Engineering
Where do they sit? What companies?
Now, hop on Linkedin.
Sort for individual profiles of people you can help.
Reach out to them directly. Engage in a live conversation.
Do this 100 times.
You will find competing opportunities.
It's based on a time proven Sales Prospecting formula. Used especially when determining Product/Market fit.
NOTHING will happen until someone reaches out and expresses an interest.
Understand you have two very different dynamics at play.
A) Companies That Need Talent
B) Talent That Need/Want Work
The expectation that employers will accurately articulate what they want-- when they want it; has a basic human flaw.
Even seasoned executives have trouble expressing what they truly WANT.
Similar problem for Job Seekers, who often have trouble positioning their own strengths and abilities on paper.
Efficiency success models are hard to find in this space -
Google famously built a top-tier employer brand. Yet people who have lived through their application/interview and hiring process describe it as dreadful. Humans still drive the hiring engine.
You might have realized that I added the qualifying adjective “unstructured” to the claim about interviews (there is plenty of empirical literature). Structured interviews are more predictive. However, some of the function of a structured interview can be replicated at third party test centers which give you a score. Employers can select for the best and most predictive tests in the ecosystem. Furthermore, those employers who don’t know exactly what they want are the least likely to have structured interviews. They are probably taking the unstructured route, and kidding themselves into thinking that they’re selecting for talent during the interview process. In reality, the best they are doing is soliciting a pool of candidates from good schools and prestigious companies. Don’t get me wrong, I’m not saying get rid of interviews. However, most interviews should be structured, and companies should outsource most of the process so that:
- they can interview more candidates
- and more of those candidates would be a good fit going in
Yet, that's exactly how Hiring Decisions at the highest levels are made.
>https://www.cnbc.com/2017/05/31/google-larry-page-and-sergey...
Depends entirely your valuation formula.
If six months of networking yielded a $300K/year job, lasting 3 years.
Would that be worth your time?
If your current income is zero, with mounting debt— the time invested is well worth your ROI.
This formula works even at minimum wage levels.
I recently spoke with someone who networked and got a great job at a top MBB consulting firm, but she was days away from giving up and settling for a job in a non-target industry that pays half. She tried to network with less highly ranked consulting firms and it didn’t work out. How arbitrary. Her projected net worth jumped by millions in the span of days, with no underlying value creation. The whole process really is an unmitigated disaster.
Self-Limiting thinking.
Unless you're a rules, systems, and process guy.
This is a real life Kobayashi Maru test.
Do you know empirically that that’s the ideal way to screen candidates? Because that would be the only good reason for so many companies to hire in that manner. There are many great engineers who, precisely because of the traits that make them great, would “fail” that Kobayashi Maru test and settle for a suboptimal role. Not only do they lose out on experience befitting of their aptitude, but the company loses out on the best candidate, and the economy loses out on the surplus created by ideal labor matching.
The system is NOT designed for ideal labor matching.
Not unlike the Test.
The job-seeker (applicant) and Employer are working at cross-purposes.
Incidentally, many Hiring Executives understand this problem.
And are often open to dialoguing with people trying to circumvent the HR black hole.
Manage or Be Managed is the proposition.
Either hunt for work (people you can help)
or passively hope someone will notice you.
Sure, the job search "game" cannot be completely eliminated. There will always be some fuzziness. However, the fuzziness can be greatly reduced on both sides, and that would be immensely valuable. You have way more faith in the value of the current job search game than I do.
If you're the kind of person to be a friend, you'd likely be hired. If you're the kind of stranger looking for a job, you'd be unlikely to be hired. If you're the kind of person to hang out on HN or some Facebook/Slack meme groups, we couldn't afford your salary.
Out of that, it would be roughly 90% of applicants who don't qualify, 5% who we can't afford, and somewhere in between which we just settle for. Many new graduates can't do FizzBuzz, many people who apply for a senior position can't reverse a linked list.
I think it would still help for a company like that to display stats. For example, if you’re a qualified candidate who is applying early, you’d see an abnormally high probability of callback. If you’re an unqualified candidate, you’d see a very low probability of callback. I mentioned that ML can help calculate these probabilities. In addition, I think it might help for companies to secretly encode “hard” job requirements. Most requirements are in fact soft, but the applicant never knows which is which. If they don’t meet a hard requirement, they should see a 0% probability of callback. Full stop. ML would do a good job of assigning importance weights to each of the requirements which aren’t encoded as “hard,” but it would start from weights provided by the employer when there isn’t enough data.
I would gently suggest that it is a mistake to think in terms of unqualified vs highly-qualified candidates and ML for "callback probability". I don't want a good, but not FANG-level, candidate to NOT apply for our standard software engineer opening just because 74 people already did.
Why? We had 60+ candidates for a recent posting for a junior developer role with some experience - think 1-2 years or some good school projects. Somewhere around 45-50 of those applicants were easily HARD-FLAGGED don't interview at all after a quick cover-letter and resume review by a technical person using a rubric. That rubric was scoped only to weed out applicants with absolutely no meaningful experience.
While I also hate the laundry-list approach for job listings (Must Have: Expert Java and C#, Spring ORM and ActiveRecord skills with deep understanding of React internals), I think the best way to attack that is with a short, but focused cover letter. That should include a short paragraph or two about a candidate's hands-on experience and speculate how their experience might apply to the position. I guess what I am driving at is that this idea of ML assigning importance weights to "soft" vs "hard" requirements is, at best, just another low-value signal in job postings that are already full of low-value signal.
A third party ecosystem of skill tests could emerge, some of which would be more prestigious/respectable than others. Some would have a binary output, and some would give you a score that is benchmarked against the population of test takers. This would only really be necessary for more technical skills. I’m not talking about rinky dinky khaki cubicle test centers. I’m talking about new age corporations with $1B market cap that develop an unparalleled ability to assess skills so that corporate HR departments don’t have to. These testing companies would be part of the ecosystem developed by the theoretical ideal, statistically-oriented, applicant friendly job platform that I’m talking about.
We did reject one smart guy for an Angular post, because his only technical skill was WordPress and the gap for that is just too big. But we interviewed people with experience with only PHP and no JS. Skills are learnable.
There's also weird stuff like rejecting people because their expected salary is too low. It's a pattern that those with low salary expectations performed worse technically than juniors.
0% callback is also rare because of that. I got my first, web dev job, with no experience in anything other than HTML and an EE degree. Rejection rates for a role are 100% until the first hire.
In the group between "don't qualify" and "can't afford", we have a hard time with candidates who are unable (or maybe lack the communication skills) to effectively talk about how their experience+potential are a good match for even a generic job opening.
We don't do any algorithmic/challenge screening during interviews.
A common interview fail for us is to have a candidate that can't generalize from or build on something from his/her resume. This usually comes out during "tell us about project X that you worked on" and comments from our side like "oh, that sounds like project Y here. But Y is different this way - what are your thoughts about how to handle <insert small-scoped, specific difference>?"
Let’s say the applicant is a senior engineer but you’re afraid they aren’t keeping up with the latest frameworks. And let’s say that the employer (or the ML also) assigned high importance weights to those frameworks. Here’s what you’d see happen:
- The senior engineer with 15 years of experience at several BigCos but doesn’t have experience in that framework will see a low probability of callback
- The senior engineer with 10 years of experience and only one BigCo who’s skilled with that framework (the right keywords, and 2 MIT edX certificates in it) will see a high probability of callback.
What’s exciting is that this will encourage people to acquire exactly the skills that will help them get the job that they want. The applicants would be able to do scenario analysis on the job platform and see how much better their chances would be if they acquire a certain skill set. This would allow them to tune out the noise and focus on what it takes to get the type of job they want. Of course, the more senior the applicant is, the more accomplishments at past jobs matter. Even then though, senior applicants usually only display a fraction of their past experience. The platform can show their callback probability change in real time if they add a meaningful past experience or accomplishment.
The possibilities are endless. Are you considering doing a XY boot camp because you think it might get you into Z Co? Think again, with your experience + that boot camp, you will have a 2% chance of getting into Z Co and 5% chance of getting a similar role with similar pay. That alone would improve the educational and vocational pipeline.
Note that I am carefully NOT saying, oh the candidate knows javascript, but not vue.js, so they don't fit for us. I am also not saying "oh, if only candidates knew we wanted them to get vue.js certified and it would be more likely we would put them in our interview loop."
I don't think the problem of how candidates can better understand why they are or are not getting callbacks from employers maps very well to any stats-based measurement of job posting descriptions against applicant resumes/CV/LinkedIn profiles.
My only thoughts on the hiring process are really anecdotal, but I believe a few things to be true.
Is that something that you expect people to be able to do? If so, what does it tell you about them? If they don't know what a linked list is or how to reverse it, what does that tell you about their real-world coding knowledge or lack thereof?
For example, I've been in the industry for 15 years and I've never had to construct a linked list (much less reverse one) in my career. I have had to build APIs, construct DB queries, create distributed systems, etc, etc. So I naturally wonder why constructing a linked list might result in rejection for a job, unless it's something you're actually expected to do on the job for that role.
Am I missing something?
Oh, to note, it's a code modification test, not a whiteboard algorithm thing.
Thanks!
Moreover, I think LinkedIn could do a better job of presenting other statistics to job candidates and reducing noise on the platform. They have done little to mitigate the feeling that your resume is most likely to disappear into a black hole. When I apply to a job, I want a good approximation of how likely it is that I even get a response. As it stands, companies have zero incentive not to waste your time, and that is actually bad for the both of you. There is more discussion on this below.