MATCH INDEX KILL THE JOB SEARCH

MASTER LIST OF KEYWORDS = 100 / Contained in ERP Function | Keywords = 1,000

Keywords in Pooja’s Resume	ERP Function: Finance, Analytics, Developer                                                 Date: 10-11-03
	Oracle	Cummins	Wipro	Info	Sterlite		XYZ	ABC	LMN
A	Ö	Ö		Ö
B		Ö		Ö
C	Ö	Ö	Ö	Ö	Ö
D				Ö
E	Ö		Ö	Ö
F			Ö	Ö	Ö
G	Ö		Ö	Ö
	UNDERLYING PREMISE / ASSUMPTION • Any given resume (Candidate) would match a large no of job advt (jobs) int to verify degrees. Job-descriptions/ Man-specifications/ skills requirements etc. mentioned in job-advts. Are broad enough to match/attract a whole lot of candidates, each of whom finds, some “degree  of match” between the advertised jobs and his own skills/acknowledge/experience/qualification etc. etc.   • This implies that, whereas a candidate may consider a given job-advt to be an IDEAL job for him (100% match between what he possesses & what that job advt. presents / requires) there will be some other jobs, job advts which he thinks are an excellent match (80%) or Good match 80% or Good match (60%) or a FAIR match 40% or a POOR match 20%   Questions: 1) So simply by looking/reading a job-ad (a whole lot of these, really), how exactly does the candidate’s reachsuchcondition abot 100%-80%-60%-40%-20% MATCHES? 2) what prescisiely is the process (apparently intuitive) that his brain employs, to arrive at such Conclusuions? 3) can we design / develop a self – learning ? Software, that can MIMIC this process & reach draw (mathematical / Statistical) Conclusions?
80			4/80	7/80	2/80	20/80	60/80	400/800	35/80
0.6	.003					.02	.52	.449	.30

 

ANSWERS

➤ Candidate (human) brain COMPARES the keywords contained in any given job-ads  with his own skills/knowledge/Exp/goals (*which two are really keywords contained in his resume!). Of course, he does not do it consciously.

    → make a list of keywords in a given job-ad

 

and then match/compare, as to how many of these (Du-sets) match. But he does this at a sub-conscious level + forms some sort of global/overall impression.

This PERCENTAGE-MATCH.

➤ Equally sub-consciously, he (his brain) assigns "WEIGHTAGES" to different keywords (in the job-ads).

→ In an advt for MATERIAL MANAGER’s position, the candidate’s brain tells him that the keyword Supply Chain Management” carries a higher weightage than the keyword “Discounted Cash Flow!” So his brain KNOWS (possibly thru reading of hundreds of job-ads for the position of MATERIAL MANAGER!) that what are “major” keywords for this position, and what are less important keywords. And obviously, from these FREQUENCY of USAGE/OCCURRENCE of keywords, over thousands of positions, vacancies, the human brain has formed RULES (obviously undocumented!) about the relative-importance (more than/less than) of thousands of keywords in the context of hundreds of jobs/positions!

And one such rule will be:

➤ The keyword “Discounted Cash Flow” carries a higher weightage for the position (job) of a FINANCE MANAGER, as compared to the keyword “Supply Chain Management.”

So the rule reverses itself, based on the “Job/Position” being advertised!

➤ So the weightage (importance) that a human brain assigns to any given keyword is NOT absolute/standalone/independent.

The weightage is *dependent/relative*!

So the same keyword would have different weightage when used in relation to different JOBS/POSITIONS (i.e., Vacancy-Names).

Not only that.

Even within a given FUNCTION (e.g., Material Management), the same word (e.g., Supply Chain Management), would have different weightages depending upon hierarchy/designation-level.

 "Supply Chain Management" would have, let us say, 0.2 weightage for the position of **Stores Officer**.

- A weightage of 0.4 for position of **Purchase Engineer**

 - A weightage of 0.6 Purchase Manager

 - A weightage of 0.8 Materials Manager etc.

➤ So, we have a following MODEL

 

                  (Graph illustration)

    - Keywords (10,000?)

    - Function (5.0?)

    - Basic Levels (10) (or hierarchy levels/actual designations)

    - (2000?)

➤ The total Combinations (10000 × 2000 × 50 = 10,000,000,000 (1 Billion)) are staggering!

➤ But human brain is a *marvellous computer*! It has some very *brilliant* "Approximation Algorithms" which cut out these clutter & quickly arrive at some *BROAD conclusions*. Human brain is also having a billion neurons and is a *parallel-processing machine*.

➤ Till we can afford such complex/huge Computers and equally complex   software we must make do with our existing *P4 machines* or simple statistical software packages.

➤ Fortunately, for our Function Profile Graphs (for resumes) we have already selected "keywords" & also computed their "weightages".

➤ In Phase I we could:

  ➝ Distribute/divide/segregate thousands of Job-Advt. **Function-wise** (subcategories)

  ➝ Using above-mentioned keywords & weightages, compute the *RAW-SCORE* of

     each Job-Advt within a given **FUNCTION**.

  ➝ Plot:

 Now, let's take Resume for Mr. Hidhe (also belonging to Mat-Mgmt function) & find out what is his *RAW-SCORE*.

Let us say it is **40**.

Hence, Matches (Resume) **40** Raw Score is **100% match** with all those *Job-Advt.* which have

*Raw-Score* = **40**.

And we know there are **60** of them.

But Mr. Hidhe's resume with raw-score of **40** is only **50% match** with (maybe) 10 job-adverts whose raw-scores are **80**.

 

By repeating this process we can come up with a frequency distribution graph as shown on *pt.1*, where we can say:

If we want Mahatre’s resume to match with Job-advts.	Then no of job advts available in our database having such Match percentage	Becase
100%	60	All 60 job advts have raw score of 40, (what Mahatre’s resume got )
50%	10	All 10 Job advts have raw score of score of 80

 

Please remember that in **Phase I** we are taking into consideration, just ONE criteria of **FUNCTION**, and comparing **RAW-SCORES**, scored by RESUMES and raw-scores scored by **JOB-ADVTs** both belonging to the same **FUNCTION**.

We are totally ignoring:

➤ Designation (Actual) or Designation Levels in order to simplify calculations/plotting.

This is a good beginning. In **Phase II** we will further refine by adding the dimension of **Designation Level** & work-out Keyword weightages for each UNIQUE combination of **Function AND Design-Level**.

But even in **Phase I**, we will be offering a **SCIENTIFIC / SYSTEMATIC / LOGIC-BASED / STATISTICALLY VALID** matchmaking.

**Match-Dreams**

➤ This is a *GREAT* convenience for a Job-Seeker who is on the job-site often today.

➤ One-shot & He knows *not just weightages* but also *availability* for **you the moment you post your resume!** Detect the different/latest **KILL JOB-SEARCH**!