Lean Hiring Aided by Machine Learning December 18, 2014 Presented by Vinayak Joglekar, Co-Founder and CTO, Synerzip

Confidential Discussion Topics 1.The Problem 2.Lean Hiring 3.Resume Ranking - Choice of Algorithm 4.Data Acquisition and Cleaning Challenges 5.Initial Results 6.Improving Accuracy 7.The Road Ahead December 18, 2014

Confidential Needle in the Haystack Situation 1.Waste in the process of hiring = resumes reviewed but not selected + candidates interviewed but not selected. 2.Every candidate “hypes” the resume to a certain extent. Much time is wasted reading pages of hyperbole to discover the grain of truth. 3.Hiring managers have other priorities - their precious time is wasted in interviewing unsuitable candidates whose resumes look good. December 18, 2014Confidential

Can I see some more resumes? Send Some more Some more please Is this all you have? Week 1 Week 2 Week 3 Feedback after 4 weeks Myth: More choice = better selection Reality: More choice = waste + delay + confusion More Choice Isn’t Always Better December 18, 2014

Confidential Make Them Jump through Hoops 1.Common response to the problem is to have a strict filtration process consisting of series of tests and interviews. 2.Good candidates are often not actively looking for a change. They get turned off by the long evaluation process. 3.The evaluation process is often flawed with too much stress on specific skills than abilities. Tests are susceptible to gaming. December 18, 2014Confidential

1.The Problem 2.Lean Hiring 3.Resume Ranking- Choice of Algorithm 4.Data Acquisition and Cleaning Challenges 5.Initial Results 6.Improving Accuracy 7.The Road Ahead Confidential Discussion Topics December 18, 2014

Kanban Extensively used in automobile industry. Principle: Any process consisting of a workflow can’t run faster than the bottleneck. All sub-processes that run faster than the bottleneck produce waste. Kanban ensures all sub-processes march to the drum-beat set by the bottleneck. Kanban is pull-based. A sub-process can’t pull more work than a pre-set WIP limit. ConfidentialDecember 18, 2014

Confidential Solution: Lets Limit the Choice! December 18, 2014

Confidential Challenge: Ranking Resumes Job 1Job 2Job 3Job 4 Expected Pay Experience Education Job Switches Soft skills Location Lead Time It took half a day for an experienced recruiter to rank a few resumes. He kept asking what was more important: Was it pay, soft skills or experience? The answer is that it depends on the job. Each job needs a different weightage to be assigned to each one of these attributes. Thus ranking reduces to assigning appropriate weightages to the attributes. December 18, 2014

1.The Problem 2.Lean Hiring 3.Resume Ranking- Choice of Algorithm 4.Data Acquisition and Cleaning Challenges 5.Initial Results 6.Improving Accuracy 7.The Road Ahead Confidential Discussion Topics December 18, 2014

Confidential Why Logistic Regression? Training a machine learning algorithm for resume ranking would require a significant number of resumes that are pre-ranked by a human expert. It is very difficult for human expert to rank resumes. On the other hand we have a lot of resumes that are classified as suitable or unsuitable which can be used to train the logistic regression algorithm. The graph here shows how the probability of a resume being suitable depends on the attributes (X) and the weights assigned to them (θ) h θ (X)=1/(1+e -Z ) where Z= θ 1 X 1 + θ 2 X 2 +…+ θ n X n where X 1,X 2,..,X n are various attributes and θ 1,θ 2,..,θ n are the weights assigned to them December 18, 2014

Confidential What is Decision Boundary? x 1 = experience and x 2 = pay 1 = suitable and 0 = unsuitable Observation 1: More pay reduces the probability of selection and more experience increases the same Decision boundary is an imaginary line that separates suitable & unsuitable examples In this case the line is x 1 -x 2 -3=0 Points below the line are likely to be suitable Points above the line are likely to be unsuitable Points along the line have equal probability of being suitable or unsuitable – hence h θ (X)=.5 & θ T X=0 along this line December 18, 2014

Confidential Training, Test & Validation Sets Testing is the measure the accuracy with which true positives and true negatives are predicted by the algorithm. In tests like cancer detection; false negatives can prove to be fatal. Input Supervised Learning Algorithm Training Set(60%) Output Validation Testing Validation Set (20%) Test Set (20%) Over/ under fitting? Valid Test Validation Training Available Data } 60% } 20% } December 18, 2014

Confidential Implementation Challenges Relevant experience Decision Boundary Very difficult to manually rank/ grade resumes thus we can’t use standard ranking algorithms. Small training sets, even smaller test sets. There are more than 13 attributes - experience, education, pay, location, availability, stability, current job, etc. based on which a candidate is selected or rejected. Many of these attributes are subjective and need to be quantified. There is no clear decision boundary. We addressed these challenges by using data from our ATS about 20 job openings for which more than 3000 resumes were considered and more than 400 candidates were found suitable to be called for interview. We used a sixth degree polynomial that lends itself well to render a decision boundary with an irregular shape. We quantified the subjective attributes like education, stability etc. We used every 4 th record to test and others to train the algorithm. December 18, 2014

1.The Problem 2.Lean Hiring 3.Resume Ranking- Choice of Algorithm 4.Data Acquisition and Cleaning Challenges 5.Initial Results 6.Improving Accuracy 7.The Road Ahead Confidential Discussion Topics December 18, 2014

Confidential Results and Analysis When we used the values of weightages delivered by the algorithm to predict, we could correctly predict 89% of the examples in the training set that was used to train the algorithm. The same algorithm could predict 65% of the test examples. We improved the accuracy by 9% when we used the sixth degree polynomial. We used the weightages to assign ranks and the ranking was well accepted and appreciated by hiring managers within Synerzip. We started practicing Kanban and lean hiring as ranking enabled us to put WIP limit on the number of resumes entering the hiring process. The hiring efficiency improved and we were able to fill in more positions without adding any new recruiters. December 18, 2014

Confidential Example Weightages - Analysis It turns out to be a fairly distributed set of values for weightages for various attributes. Each job opening uses independent assessment of resumes. This job opening gives extremely negative weightage to “current compensation” – this means that candidates earning well are not suitable; while its just the opposite case for most other job openings. This position assigns positive weightage to total experience but negative weightage to relevant experience. The requirement was for a broader skillset beyond just C++. December 18, 2014

Confidential Comparing Results BeforeAfter Hiring Cycle Time6 to 8 weeks3 to 4 weeks Hit RateLess than 10%Almost 50% Hiring Manager’s Time per hire 15 to 25 hours per position Less than 10 hours per position Recruiter’s time per hire Close to 100 hours per position Less than 40 hours per position Hiring MistakesLow confidenceHigh confidence December 18, 2014

1.The Problem 2.Lean Hiring 3.Resume Ranking- Choice of Algorithm 4.Data Acquisition and Cleaning Challenges 5.Initial Results 6.Improving Accuracy 7.The Road Ahead Confidential Discussion Topics December 18, 2014

Confidential Improving Accuracy We tried using regularization to avoid over-fitting. However it did not yield any improvement in accuracy. As we have the accuracy at 89% while predicting the training set itself, it can be intuitively concluded that over-fitting doesn’t need to be addressed. We need to get more training data to improve accuracy. Also the training data should pertain to a period over which the job requirements are constant. It’s very hard to find job openings where more than a hundred candidates are screened. As we plan to implement lean hiring and Kanban, the chances of having large training sets is very low. We tried seeing if principal component analysis can be used to reduce the number of attributes to 2 or 3 to be able to plot. We could not get the “retained variance” anywhere close to 99%. (In fact, it was close to 50%.) December 18, 2014

1.The Problem 2.Lean Hiring 3.Resume Ranking- Choice of Algorithm 4.Data Acquisition and Cleaning Challenges 5.Initial Results 6.Improving Accuracy 7.The Road Ahead Confidential Discussion Topics December 18, 2014

Confidential Future Roadmap Create search engine app with ranking on the fly to limit the number of search results that fit within the smartphone screen with no need to scroll. Try using sixth degree polynomial with more attributes. Currently we are using it only on expected compensation and relevant experience. This will most likely improve the accuracy. Using NLP for information extraction and more precise attribute values. December 18, 2014

Hemant Elhence December 18, 2014Confidential

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Call Us for a Free Consultation! Hemant Elhence linkedin.com/company/synerzip facebook.com/Synerzip December 18, 2014Confidential