AI screening tool for a recruitment software

Overview: The client was a start-up solving the problem of managing numerous processes within a recruitment department. Hiring teams have data from a variety of sources, which are difficult to process and analyze. Our task was to structure and develop a data structure and create an AI tool for screening candidates.

Approach: Our team of data engineers, data scientists, and ML engineers started with a research phase to understand the client's requirements and identify the best approach for the project. We conducted a thorough analysis of the recruitment data landscape and the different types of data sources used in the industry. We then developed a solution architecture that could handle the complex data structures and AI models required for the project. We selected AWS as the primary provider for data storage and combined it with other ETL tools to work with different formats from multiple sources.

Once the data structure and pipelines were set up, we used semantic analysis with NLTK preprocessing to extract the necessary information for each candidate. The semantic analysis allowed us to identify the candidate's skills, experience, and education from their resume, cover letter, and social media profiles. We then developed the ML model using Keras, TensorFlow, and SKLearn to sort the candidates into predefined groups and factors, such as experience level, job title, and location. The output was presented in a structured database of candidates, which could be easily searched and filtered by hiring teams.

Key Metrics: The AI screening tool provided significant benefits for our client's recruitment process. The applicants in the system were automatically shortlisted, and the hiring teams only needed to review the screening results. 

The solutions achieved their goal of optimizing the time a recruitment team needs for manual review of the CVs of candidates and reducing it by half. The increased productivity allowed them to focus on more strategic tasks, such as conducting interviews and assessing candidate fit.

Case Studies

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