Objective
Our client, one of the top 5 tech companies in the world, wanted to:
- Design a smart search solution to optimize the search for potential candidates from a large pool of profiles. The design solution can retrieve the most appropriate candidates based on their past performance and search query pattern.
- Optimize the search results by using multistep algorithms that will narrow down the results and rank the candidates based on their likelihood of getting hired.
Solution
- Designed a solution on top of an applicant 360 data lake and replicated transactional data from the pool.
- Designed micro-batches to extract and curate features that were used to track the past performance of applicants and unique qualifiers such as the applicant’s location, past experiences, background, positivity index from answers provided, past recruitment status, etc.
- Core search engine models were trained on a regular interval (overnight batch), and trained artefacts were stored in the database. The training process used the hired status of the classifier to predict the likelihood of any candidate being shortlisted for any specific job.
- The JMI team also designed the "Search API" to take basic inputs from recruiters and retrieve the matching records. The service used AI runtime to shortlist and rank candidates further based on the probability of hiring within the context of search criteria.
Benefits
- Ensured targeted response by ranking profiles.
- AI powered search to automate first level of scrutiny and maximize time-to-value in bulk recruiting lifecycle.
Value Proposition
- The AI driven “search API “ was able to shortlist applicants with more than 90% accuracy. The solution was tested with historical data points using wide range of job profiles, funnels and applicants. The robust and scalable design of the solution outperformed the standard keyword and metadata-based search capabilities while offers low latency response.
- The solution minimized the time and effort of any recruiter to identify the ideal candidates for recruiting and window of hiring. It was primarily designed to support bulk recruitment initiatives where the recruiters need to qualify, appoint a large number of candidates for multiple positions and/ or locations within shortest possible timeline.
- The multistep AI based learning applied on validated history of applicants, their performance with interview and jobs offers deep insights on potential hires and extend first level of scrutiny as an intelligent hiring assistant. Such utilities boosted productivity significantly and minimized operational cost of the recruiting agencies.