1. The parameter scoring is based on expert analysis, feature engineering and feature selection techniques. The higher the score, the higher the probability of a Jobseeker being in the correct work domain and high suitability for the position.
2. Feature engineering is the process of using domain knowledge of the data to create features (i.e. scoring parameter) that makes machine learning algorithms work.
3. Feature Selection is the process where we automatically or manually select those features/parameters which contribute most to our prediction variable/output in which we are interested in.
4. Applying feature engineering and feature selection technique on our datasets, we found approximately 60 scoring parameters, in addition to the extension into more specific custom scoring modules.
5. Using Machine Learning Algorithms, we find scores for each parameter.

 

Techniques:

 

Feature Engineering Techniques

1. Imputation – Handle missing data, incoherent data
2. Outlier Analysis and Handling Outliers
3. Data preprocessing such as remove punctuations, stop words, etc
4. Tokenization
5. Vectorization

Feature Selection Techniques

1. Univariate or Multivariate Selection
2. Recursive feature elimination (RFE)
3. Topic Selection
4. K-Fold Cross Validation

Machine Learning Algorithm

1. Latent Dirichlet Allocation (LDA) Algorithm
2. Deep learning Neural network
3. Gibbs Sampling / Incremental Variational Inference

 

We are pleased to share the science and business logic of the Geescore™Jobseeker Scoring solution.

 

We start with what is commonly called “matching science”. This is a methodology to extract words, phrases and acronyms from both a job posting, as well as a Jobseeker resume, and to compare how this data matches.

 

Based on the data match and similarity scores, we calculate the probability of two matched words or phrases.

This is done by selecting topics or keywords from Job description and a vector of words from the resume. For each topic, joint probability distribution of relevant keywords will be formulated. The probability values show that for a particular topic, this resume would have a certain joint probability of topics/keywords and set of words from resume. Higher the probability, the prospect of resume being better suited to the Job posting..

For example, a person who has worked as a Data Scientist will have a higher probability of having Python programming in his skill set or vice versa. By calculating the sum of products of probability of each intersecting set of topics such as Domain, work experience, skillset and various other parameters, we arrive at an accurate score for a Resume with respect to a Job Description.

 

For many HRTech solutions with filtering and scoring, this is a core function. To improve matching results, many providers use machine learning to train their solution, by better classifying the data.

 

Geescore™’s Jobseeker Scoring solution uses a combination of hands-on human research and classification, alongside machine learning.

 

There are a few other features that makes Geescore™ significantly different.  When we engage with the Jobseeker online and via email, we encourage them to add more career information (ADD), share links to their portfolios and social presence (SHARE), as well as help us fix issues discovered during the scoring process (FIX). This is valuable decision-making data for Hiring Managers. In the near future we will also begin scoring this content. Right now we reward an engaged & interested Jobseeker for adding, sharing and fixing. We give them a small boost in their score.

 

Additional features of the Geescore™Jobseeker Scoring solution are applying a set of 12 + “real-world” recruiting factors that are part of our scoring, such as commuting distance, Jobseeker interest in a job posting, relevant domain expertise, and more.

 

Finally, our view of matching science is that it is just a start to developing custom scoring modules. We apply both machine learning and human research, to develop custom scoring to help avoid time spent on unsuitable Jobseekers, to improve your talent acquisition efforts.

Clients have all kinds of methods and systems to find more success when hiring Jobseekers. Some use personality or EQ testing. Others consider background checking, and most take the time to check Jobseeker references.

 

We provide some recommended Actions to consider, as a result of examining the scoring results.

 

We display a Total Score as well as a Jobseeker’s Component Scores. The Jobseeker can output a png image  of their Score, and attach it to their job application.

 

 

After a Jobseeker gets their score for a job posting, we share recommended actions for resume, job search, interviews and much more.

 

These Jobseeker actions appear in a detail popup in the scoring widget, and are sent via email.

 

Clients can view a Jobseekers’s component score inside the dashboard, and a popup shows some recommended actions to help with evaluating high-scoring Jobseekers, and improve talent acquisition results,