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- Part 1 of this series, provided business justifications and frameworks to validate success of such solution, if it were to be built.
- Part 2 of this series, discussed about data exhausts for building a models.
In this post, we will detail one of few methodologies to build such a solution.
Based on available data,
- Multiple models are developed and
- Output scores from each models are ensemble to
- Compute a final outcome of probability of selection of candidate and
- using that score and threshold slot candidates for interviews.
- Compatibility Score:
- Tokenize text to bag of words
- Remove noise and other language dialects.
- Use term frequency and inverse frequency metrics to get importance of words
- Generalize resume words and
- Find distance between candidate resume and job description.
- Competitive Score:
- Based on historical data, convert candidates resumes to features with labels of clearing interview or not.
- Build a ML Model that uses these features and
- Gives probability score that predicts if candidate may get selected.
- Culture Fitment Score:
- Cluster existing set of employee with feature baselined to candidates
- Cluster them into groups as designed (High Performers , Average Performers, Poor Performers etc, just an example)
- Slot new candidate into one of these buckets and get scores.
Ensemble all these scores to build a model, that outputs probability of candidate converting into employee and based on that slot him / her for an interview.
Mind you, we are NOT replacing interview process, our objective is only to find most probable candidate to clear interviews.