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