4 ms·

Reducing Time-to-Hire and Finding Niche Candidates via Text Mining and NLP

33 points by cl42 6y ago

cl42 6y ago
Hi folks! OP here + author of the NLP algorithms in the post. If you're hiring and want us to help you with filtering like this, we're definitely keen to pilot this or provide support in some form.
- fatnoah 6y ago
  I find this interesting. As someone with over 20 YOE, I've always eschewed the "2-page" resume guidelines. My full resume ends up being 4-5 pages, even with rigorous attention to only a 1 sentence description of each role, avoiding duplication of facts, and keeping and bullet points in the form of "Did X, resulting in Y". I've always wondered if a longer format helps me more with automated resume scoring. Also, does LinkedIn come into play here? I feel like when I show up to an interview, there's a 50/50 chance they're holding my resume vs. a printout of my LinkedIn profile.
  - cl42 6y ago
    Yesssss... That's part of the issue here. I feel like people optimize for keywords because the applicant tracking systems today optimize for keywords. By the time a hiring manager gets to a resume, you've likely been filtered several times... The challenge is those filters suck. Hence our approach to making it "conversational" or flee-flowing. Using word embedding and other clustering helps a lot. Knowing nothing else, I'd say keeping your resume to 2 pages is critical... And make your "about" section on LinkedIn really stand out with a few key accomplishments. If you'd like, shoot me your resume at hello@phaseai.com and we can give you specific feedback.
    - fatnoah 6y ago
      I appreciate the offer to help, but my general success rate is high enough that I don't want to mess with stuff. I do target my pursuit of opportunities, and I ALWAYS rewrite my resume so that it prioritizes the experiences and skills most relevant to each job. If I were to ask for anything, it'd be something to highlight those matches.
      - cl42 6y ago
        Amazing. If I had a penny for every job seeker who told me they don't do any resume customization... I'd have lots of pennies! :)
- karagenit 6y ago
  Cool stuff - reminds me of a semester project I did about a year ago using latent semantic analysis to find relationships between documents. Sounds similar to what you guys are doing, trying to pair job descriptions with the best fitting resume. I'd love to hear more about the technical details on your solution, though I suspect that's somewhat of a "trade secret" ;)
  - cl42 6y ago
    A big part of what we've been doing focuses on word embedding models and classifying individual parts of the document. The word embeddings help us look for synonyms general ideas around what the recruiter is looking for, versus what we are finding. We also score individual sections to see if the sections themselves cover all the bases or if it's the resume as a whole. We've also explored building our own universe of words/terms to help with this, but that's a 2.0 sort of dream, at this point!
- sqrt17 6y ago
  err ... it's kind of what you'd get from Textkernel or LinkedIn Recruiter, probably at a similar price. But with nicer fonts, so ok.
legerdemain 6y ago
I'm not sure... Scratch that. I'm extremely skeptical that either job descriptions or resumes can be scoured for subtle indications of fit between jobs and candidates. If your new job isn't quite what you expected or your new employee isn't working out quite the way you wanted, it's probably not because you didn't scour resumes and job descriptions minutely enough. Resumes are a very impoverished indicator of a candidate's abilities. So you "have experience" with a technology. Did you make the original decision to use it? Or were you asked to use it? Or was it in place when you got there? Were you only a consumer of its benefits? Do you understand its internals? Have you had to extend it? A technical project may take months or years, but your resume will represent it as a single line of text at best. "Year NNNN -- Company Foo -- Role Bar -- Implemented X using technology Y." Until resumes are structured like interviews, interviews are essential for getting this level of detail. Job descriptions are a very impoverished indicator of what a job is actually like. "We require N years of experience with X." Is that because this is your generic job description boilerplate? Or because your entire tech stack is inextricably tied to X? Will the candidate join a team that has inherited a legacy project that uses X? How is that team's morale? How is their turnover? If the candidate hates being on that team, how easy will you make it to switch? Are you hiring the candidate specifically with the goal of throwing people at a messy problem? Is it your responsibility to make sure the candidate succeeds? Until job interviews spend as much time revealing the company to the candidate as they spend grilling the candidate, back channel communication is essential for getting a job you like. I can't believe that ML and NLP offer new insights in this exchange. If I'm not willing to tell you something, you won't get it from me using NLP either. I've reached a point where recruiters telling me about using "cutting edge AI" results in their email getting deleted immediately. The last such service I responded to (Celential.ai) spammed me for a long time, and every job they sent me was garbage. Garbage to such a degree that it was my first experience with getting on the phone with a hiring manager and one or both of us saying, "No, there's really no point in us having this conversation, thanks," and hanging up.
- cl42 6y ago
  I agree with you and we're not trying to infer new things about people that aren't directly in the resume. Our approach is to take a more (unfortunately?) practical approach to this -- most recruiters and hiring managers simply scan most resumes for keywords, and most applicant tracking systems do the same... So it's not about predicting something from the resume, so much as making the matching process more flexible, given that most people doing the initial screening aren't doing a good job. In other words: make a very imperfect process slightly less bad today, so good candidates don't get missed. And of course, you still need to do the full set of interviews afterwards! We're not saying this is any sort of silver bullet.
soonnow 6y ago
In my opinion you are solving the wrong problem. I can scan a hundred resumes in no time. Once you've done it a few times, you can get an idea where the person on the resume is standing in their professional career. And that is pretty much all you can get out of a resume anyway. Apart from some signals that tell me a candidate is interesting (e.g. interesting side projects) I for the life of me can't tell from a resume, if a developer writes good maintainable code and I doubt that AI can. The thing that actually takes time is really the part that comes after reading the resume. Bringing people in, making appointments, interviewing, testing their quality, see if they are a fit for the company, find if they are a fit for the team.

33 points by cl42 6y ago

cl42 6y ago

Hi folks! OP here + author of the NLP algorithms in the post. If you're hiring and want us to help you with filtering like this, we're definitely keen to pilot this or provide support in some form.

fatnoah 6y ago
I find this interesting. As someone with over 20 YOE, I've always eschewed the "2-page" resume guidelines. My full resume ends up being 4-5 pages, even with rigorous attention to only a 1 sentence description of each role, avoiding duplication of facts, and keeping and bullet points in the form of "Did X, resulting in Y". I've always wondered if a longer format helps me more with automated resume scoring. Also, does LinkedIn come into play here? I feel like when I show up to an interview, there's a 50/50 chance they're holding my resume vs. a printout of my LinkedIn profile.
- cl42 6y ago
  Yesssss... That's part of the issue here. I feel like people optimize for keywords because the applicant tracking systems today optimize for keywords. By the time a hiring manager gets to a resume, you've likely been filtered several times... The challenge is those filters suck. Hence our approach to making it "conversational" or flee-flowing. Using word embedding and other clustering helps a lot. Knowing nothing else, I'd say keeping your resume to 2 pages is critical... And make your "about" section on LinkedIn really stand out with a few key accomplishments. If you'd like, shoot me your resume at hello@phaseai.com and we can give you specific feedback.
  - fatnoah 6y ago
    I appreciate the offer to help, but my general success rate is high enough that I don't want to mess with stuff. I do target my pursuit of opportunities, and I ALWAYS rewrite my resume so that it prioritizes the experiences and skills most relevant to each job. If I were to ask for anything, it'd be something to highlight those matches.
    - cl42 6y ago
      Amazing. If I had a penny for every job seeker who told me they don't do any resume customization... I'd have lots of pennies! :)
karagenit 6y ago
Cool stuff - reminds me of a semester project I did about a year ago using latent semantic analysis to find relationships between documents. Sounds similar to what you guys are doing, trying to pair job descriptions with the best fitting resume. I'd love to hear more about the technical details on your solution, though I suspect that's somewhat of a "trade secret" ;)
- cl42 6y ago
  A big part of what we've been doing focuses on word embedding models and classifying individual parts of the document. The word embeddings help us look for synonyms general ideas around what the recruiter is looking for, versus what we are finding. We also score individual sections to see if the sections themselves cover all the bases or if it's the resume as a whole. We've also explored building our own universe of words/terms to help with this, but that's a 2.0 sort of dream, at this point!
sqrt17 6y ago
err ... it's kind of what you'd get from Textkernel or LinkedIn Recruiter, probably at a similar price. But with nicer fonts, so ok.

legerdemain 6y ago

I'm not sure... Scratch that. I'm extremely skeptical that either job descriptions or resumes can be scoured for subtle indications of fit between jobs and candidates. If your new job isn't quite what you expected or your new employee isn't working out quite the way you wanted, it's probably not because you didn't scour resumes and job descriptions minutely enough. Resumes are a very impoverished indicator of a candidate's abilities. So you "have experience" with a technology. Did you make the original decision to use it? Or were you asked to use it? Or was it in place when you got there? Were you only a consumer of its benefits? Do you understand its internals? Have you had to extend it? A technical project may take months or years, but your resume will represent it as a single line of text at best. "Year NNNN -- Company Foo -- Role Bar -- Implemented X using technology Y." Until resumes are structured like interviews, interviews are essential for getting this level of detail. Job descriptions are a very impoverished indicator of what a job is actually like. "We require N years of experience with X." Is that because this is your generic job description boilerplate? Or because your entire tech stack is inextricably tied to X? Will the candidate join a team that has inherited a legacy project that uses X? How is that team's morale? How is their turnover? If the candidate hates being on that team, how easy will you make it to switch? Are you hiring the candidate specifically with the goal of throwing people at a messy problem? Is it your responsibility to make sure the candidate succeeds? Until job interviews spend as much time revealing the company to the candidate as they spend grilling the candidate, back channel communication is essential for getting a job you like. I can't believe that ML and NLP offer new insights in this exchange. If I'm not willing to tell you something, you won't get it from me using NLP either. I've reached a point where recruiters telling me about using "cutting edge AI" results in their email getting deleted immediately. The last such service I responded to (Celential.ai) spammed me for a long time, and every job they sent me was garbage. Garbage to such a degree that it was my first experience with getting on the phone with a hiring manager and one or both of us saying, "No, there's really no point in us having this conversation, thanks," and hanging up.

cl42 6y ago
I agree with you and we're not trying to infer new things about people that aren't directly in the resume. Our approach is to take a more (unfortunately?) practical approach to this -- most recruiters and hiring managers simply scan most resumes for keywords, and most applicant tracking systems do the same... So it's not about predicting something from the resume, so much as making the matching process more flexible, given that most people doing the initial screening aren't doing a good job. In other words: make a very imperfect process slightly less bad today, so good candidates don't get missed. And of course, you still need to do the full set of interviews afterwards! We're not saying this is any sort of silver bullet.

soonnow 6y ago

In my opinion you are solving the wrong problem. I can scan a hundred resumes in no time. Once you've done it a few times, you can get an idea where the person on the resume is standing in their professional career. And that is pretty much all you can get out of a resume anyway. Apart from some signals that tell me a candidate is interesting (e.g. interesting side projects) I for the life of me can't tell from a resume, if a developer writes good maintainable code and I doubt that AI can. The thing that actually takes time is really the part that comes after reading the resume. Bringing people in, making appointments, interviewing, testing their quality, see if they are a fit for the company, find if they are a fit for the team.