4 ms·

Ask HN: Why don't employers post an applicant-to-job ratio?

9 points by keenmaster 7y ago

PaulHoule 7y ago
How is that useful? 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.
gshdg 7y ago
What value would the employer get from providing that info?
- keenmaster 7y ago
  Sorry I had to repost due to the "Ask HN" tag in the title not working. See my other comment for the logic behind my question.
- muzani 7y ago
  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.
keenmaster 7y ago
Example with made up numbers: 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 I believe a more applicant-friendly job platform, which publishes more statistics, aggressively penalizes outdated/lame listings, and incentivizes updates from the employer will attract more applicants.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.
JSeymourATL 7y ago
> 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. 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.
- keenmaster 7y ago
  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.
  - JSeymourATL 7y ago
    > 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.
    - keenmaster 7y ago
muzani 7y ago
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.
- keenmaster 7y ago
  “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.
  - tom_b 7y ago
    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.
codegeek 7y ago
Linkedin job postings do show the number of applicants for a job.
- keenmaster 7y ago
  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.

9 points by keenmaster 7y ago

How is that useful? 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.

gshdg 7y ago

What value would the employer get from providing that info?

keenmaster 7y ago
Sorry I had to repost due to the "Ask HN" tag in the title not working. See my other comment for the logic behind my question.
muzani 7y ago
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.

keenmaster 7y ago

Example with made up numbers: 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.

JSeymourATL 7y ago

> 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. 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.

keenmaster 7y ago
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.
- JSeymourATL 7y ago
  > 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.
  - keenmaster 7y ago

muzani 7y ago

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.

keenmaster 7y ago
“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.
- tom_b 7y ago
  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.

codegeek 7y ago

Linkedin job postings do show the number of applicants for a job.

keenmaster 7y ago
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.