Bots to Hire Humans. Trained by Humans to hire human–Part 3

Disclaimer:

• THIS BLOG DOES NOT REPRESENT THOUGHTS, INTENTIONS, PLANS AND STRATEGIES OF MY EMPLOYER DIRECTLY OR INDIRECTLY. IT IS SOLE MY OPINION WITH NO WARRATIES AND FOR FUN .

• BLOG ONLY CONSIDERS TECHNOLOGY ASPECT OF SOLUTIONING AND DOES NOT INFER / UNDERSTAND IN ANYWAY SOCIAL OR OTHER RELATED IMPACTS.

• Part 1 of this series, provided business justifications and frameworks to validate success of such solution, if it were to be built.
• Part 2 of this series, discussed about data exhausts for building a models.

In this post, we will detail one of few methodologies to build such a solution.

Based on available data,

• Multiple models are developed and
• Output scores from each models are ensemble to
• Compute a final outcome of probability of  selection of candidate and
• using that score and threshold slot candidates for interviews.

• Compatibility Score:
• Tokenize text to bag of words
• Remove noise and other language dialects.
• Use term frequency and inverse frequency metrics to get importance of words
• Generalize resume words and
• Find distance between candidate resume and job description.
• Competitive Score:
• Based on historical data, convert candidates resumes to features with labels of clearing interview or not.
• Build a ML Model that uses these features and
• Gives probability score that predicts if candidate may get selected.
• Culture Fitment Score:
• Cluster existing set of employee with feature baselined to candidates
• Cluster them into groups as designed (High Performers , Average Performers, Poor Performers etc, just an example)
• Slot new candidate into one of these buckets and get scores.

Ensemble all these scores to build a model, that outputs probability of candidate converting into employee and based on that slot him  / her for an interview.

Mind you, we are NOT replacing interview process, our objective is only to find most probable candidate to clear interviews.

Bots to Hire Humans. Trained by Humans to hire human – Part 2

Blog subject may be look more of click bait ( actually no hits better ) considering position taken by industry stalwarts recently.

Disclaimer:

• THIS BLOG DOES NOT REPRESENT THOUGHTS, INTENTIONS, PLANS AND STRATEGIES OF MY EMPLOYER DIRECTLY OR INDIRECTLY. IT IS SOLE MY OPINION WITH NO WARRATIES AND FOR FUN .
• BLOG ONLY CONSIDERS TECHNOLOGY ASPECT OF SOLUTIONING AND DOES NOT INFER / UNDERSTAND IN ANYWAY SOCIAL OR OTHER RELATED IMPACTS.

Part 1 of this series, provided business justifications and frameworks to validate success of such solution, if it were to be built.

Moving along, first step in building solution is identifying sources of data. With computing becoming ubiquitous, every candidate leaves a data trail, aka data exhaust,  that when tapped can be transformed into ambient intelligence about candidates to be screened. DIKW (Data, Information, Knowledge, Wisdom) pyramid was backbone of lot of analytics product architectures. Thinking in most generalist sense, converting Data to information, in an easier, intuitive and productive way  is what is sole existential reason for products like Power BI , Tableau, Qlick and plethora of others. was business models. With Machine Learning now generally becoming main stream in solutions, gap between data and information is slowly narrowing. Computing and modeling advancements further blur between data and information and machines are used to identify patterns in data and leave to humans converting that information to useful knowledge.

Credit: https://en.wikipedia.org/wiki/DIKW_pyramid

For our candidate screening automated solution, sources of data can be categorized into

• Resume
• Social (LinkedIn, Facebooks)
• Blogs
• many others, more the merrier.

Multiple ways of classifications are possible. Resume data can be categorized as claim and other supporting data sources can be used either for validation of claim or improving knowledge about candidate. Below is process flow for a hiring process.

• Job Description details what is needed by an organization.
• Resume claims that candidate matches to role detailed Job description.
• Screening , subsequent interviews till final selection are steps to validate candidate claims (as detailed in resumes)

Aligning to earlier stated goal of rejecting candidate early, social media and data other than resume, can be used to validate candidates claims through resumes. And based on such validations, knowledge about candidate enhances and enables system to take informed decisions to screen a candidate. But there is a downside to this approach, what if a candidate does not belong to social network sites, does not blog and tries to keep digital exhaust to minimum. Information bias is key problems for such solutions, as data in social media is itself biased. Good related articles in information bias,

• AI being racist.
• https://www.forbes.com/sites/bridaineparnell/2014/11/27/scientists-warn-about-bias-in-the-facebook-and-twitter-data-used-in-millions-of-studies/#769b418e5de8
• https://www.theguardian.com/media-network/media-network-blog/2014/may/13/internet-confirmation-bias
• https://blogs.microsoft.com/?p=61401

Here below depicts one inputs for HR screening solution, ie is information about candidate.

As afore mentioned, hiring process starts with Role / Job Description details. Candidate applying CLAIMING he / she has right skillsets. Followed by Screening and Interviewing that validates claims of candidates and thus provide knowledge about him / her to take a correct decisions. So remaining sources of information could be leveraged for solution could include

• Job Description
• Interview process (Have you handled stress interviews??)
• Interviewer (may be having bad day??)

First step in screening process it to match resume claims with Job description (JD) of opening. If you are for solution, next post deals with how to automatically identify nearness of resume with job description and challenges in doing that.

Bots to Hire Humans. Trained by humans to hire human.

Blog subject may be look more of click bait ( actually no hits better ) considering position taken by industry stalwarts recently.

Disclaimer:

• THIS BLOG DOES NOT REPRESENT THOUGHTS, INTENTIONS, PLANS AND STRATEGIES OF MY EMPLOYER DIRECTLY OR INDIRECTLY. IT IS SOLE MY OPINION WITH NO WARRATIES AND FOR FUN .
• BLOG ONLY CONSIDERS TECHNOLOGY ASPECT OF SOLUTIONING AND DOES NOT INFER / UNDERSTAND IN ANYWAY SOCIAL OR OTHER RELATED IMPACTS.

Gone are days where IT companies would hire in bulk. Roll back few years back, when automation was still not present, IT and ITES companies would hire in hordes. But even then number of candidates interviewed were high with respect to number of selected and ultimately joined. Here is simple math that details problem, thus an opportunity for a product. Hit ratio for hiring refers to  Number of Candidates (Selected / Joined) / Number of Candidates Screened. As I have learnt, it is around 10 – 15% for good companies, i.e out of 100 candidates screened, only 10 to 15 are selected. Implies 85 – 90 candidates are rejected at various stages of interview process. Lets add money angle to drive point here.

Imagine company X hires 10K employees every year with hit ratio of 15%. For 10 K they need to screen at least 150K. Conservatively even if 100 K of candidates could have been rejected early in hiring cycle, more economical for company. Assuming 100/- per candidate whether selected or rejected, it turns out 100 K * 100 /- equals approx. 10 Crore rupees. Please keep in mind this is not verified but a gist of discussion with few HR teams at a symposium. So, there atleast seems a justification in thinking a solution to see if there could be a way to implement automated screening process that ensure candidates, who ultimately fail selection process, fail early in cycle which could be a win – win for both candidate and company.  

This post will initiate slides with commentary and in later posts detail each of these slides in detail.

1. Everyone Wants Super Employee

Slide is self explanatory.. Right Talent, Just in Time with best fit. Simple one way to explain it is “Pink Squirrel”. Are you pink squirrel? Pink Squirrel refers to a candidate who perfectly matches a job’s requirements in every way, from education, to skills, to personality.

Credit: https://leadblog.ca/2014/05/01/purple-squirrel/

2. Business metrics for Machine Learning models: Is Machine Learning actually helping business or is everyone just travelling on hype cycle with peak of inflated expectation. https://en.wikipedia.org/wiki/Hype_cycle

One of important aspects that I have learned is for Machine Learning tasks, defining business metrics that capture success is very important, else it ends up good project to have on resumes . So here in this slide we define metrics that define success of machine learning project if it were to be introduced to HR hiring cycle.

 

Few points from above slide,

• Reach Ratio is also efficiency of hiring process.
• Conducting first level screening manually requires lots of eyeballs to scan resumes and select possible candidates for next level screening. Manually implies cost and limits (of course no one is hiring in millions ).
• As more candidate resumes pour in, to increase efficiency (more resumes screened), effectiveness is impacted.
• Candidate Hit Ratio is effectiveness of screening in hiring process.
• If a candidate is screened and slotted for interview, screening is perfect. 

Now Reach Ratio tending towards Zero implies for a job opening, hire as many candidates as possible (more the better), and effectively choose a single candidate who will clear all interview and get selected and will join. Being realistic, above statement is nirvana state and not practically possible, even if solution predicts with 70 – 80% accuracy, that is a huge gain for hiring process. 

If you are for solution, next post deals with data exhaust from candidates..