The document discusses using probabilistic graphical models to better understand the job market and recruitment process. It proposes that a recruitment expert can define the domain for a job and identify multiple suitable job specifications based on analyzing resumes available in the market, rather than relying only on initial expert specs. This allows recruiters to understand what candidates are actually available and avoid surprises. A graphical model is presented showing relevant candidate attributes as nodes to help quantify these relationships.

1. Understanding the Job market
using Probabilistic Graphical
Models
20 June 2012 Venkatesh Umaashankar

2. Job Market

Hiring managers go out to a job market thinking
they want to hire Talent ABC; the reality is that
often what they'll end up hiring is ABX, AXY, or
even XYZ. The job market informs, and can
shift what the hiring manager ends up hiring.
what the job spec initially says is not what the
candidate ends up possessing.

Source: http://www.quora.com/Hiring/Does-the-hidden-job-market-actually-exist
20 June 2012 Venkatesh Umaashankar

3. Can we do better?

Recruitment is always driven by what is
available in the job market.

Can we derive the multiple good job specs for a
specific job, from the job market itself rather
than limiting it to specs given by an expert?

Can a expert refine the job specs based on
what is available in the job market?
20 June 2012 Venkatesh Umaashankar

4. Leadership Positions in a fast growing
analytics product firm – Job Specs

1. 6 – 10 years of experience

2. Analytics / predictive modeling experience

3. R, SAS, Weka, Java, Python, Matlab

4. Solve any 1 case study of your choice from
www.kaggle.com and send us the way you
solved it .
20 June 2012 Venkatesh Umaashankar

5. Who you may end up hiring

B.E/B.Tech CS Major, 4 yrs, employers: IBM, SAS, Team
lead, skillset: R, SAS, Java, C++, Weka, matlab. KDD cup
participant

MBA, 10 Yrs experienced, Startup experience, Business
Intelligence and Analytics head

M.Tech, IIT, 3+ years as software developer, MTech
dissertation in machine learning, few publications in
machine learning. Current employer: Oracle

M.Sc Mathematics, Anna university, 5 years experience in
SAS, regression and predictive modeling.
20 June 2012 Venkatesh Umaashankar

6. How to do better

A recruitment expert can define a generalized
domain for a particular job.

Look in the job market ( by scanning the
resumes of the persons who have applied for
the job or the resumes available in the
database of a job portal) and identify multiple
good job specs for the job that are available in
the job market which satisfy the defined
domain.
20 June 2012 Venkatesh Umaashankar

7. Advantages

Even before interviewing the applicants, the recruitment panel
can understand what the market has really got to offer them.

Identify, some or many apt job specs which are available in the
market and are scarce.

Ignore, widely available or less suitable job specs and interview
only the persons who match good job specs.

Refine the intial job specs and come up with multiple job specs,
that are actually available in the market and are not just
hypothetical.

No more surprises or disappoints during the interview.

Interviews can be arranged only if market really has some
suitable people.
20 June 2012 Venkatesh Umaashankar

8. Graphical Model for Leadership
Positions in a analytics firm
TOP university
Big names in Current
and Ex employers
Startup
experience
Maths Major
Open source
software exposure
CS Major
MBA Data mining
Experience
3+ Experienced
Experince in
production systems Kaggle.com
publications

Blue nodes are boolean, can take values Y or N Prospective
 20 June 2012
Green Node is unary, can take only one value Venkatesh Umaashankar Kdd cup
Candidate

9. Probabilistic Queries on the
Graphical model

The graphical model that we saw in the previous slide is a
Bayesian network.

We can consider all the applicants as prospective candidates
and calculate the conditional probability distribution for each
node, using the resumes of the applicants.

We can make probabilistic queries like shown below to

What is the probability of a finding a person who has 3+
experience, MBA, open source exposure and data mining
experience.

What is the probability of person having startup experience,
given he has data mining experience.
20 June 2012 Venkatesh Umaashankar