INTRO AND CLIENT BACKGROUND
Matics Analytics recently collaborated with a prominent software company headquartered in London, UK.
The company offers an advanced Applicant Tracking System (ATS) designed to streamline hiring processes for organizations worldwide.
Their product efficiently manages job postings, resume submissions, and candidate tracking throughout the recruitment lifecycle.
Business Challenges
The company faced critical challenges in their product improvement:
Inefficient Screening Process: Leading to an average time-to-hire of 45 days.
Lack of Data-Driven Insights: Necessitating improved decision-making in hiring.
High Candidate Drop-Off Rates: Resulting in potential loss of top talent.
To address these issues, they aimed to enhance their recruitment efficiency by developing a predictive system capable of estimating the likelihood of resume submissions leading to interviews and eventual hiring.
Business Goals
Reduce time-to-hire by 30%, accurately identifying high-potential candidates..
Enhancing the quality of candidate selection to improve recruitment outcomes.
Optimizing recruiter efforts and focusing resources on promising candidates.
Key Technical Challenges:
Handling Unstructured Data: From resumes, job descriptions, profiles, interview outcomes, ATS.
Addressing Bias: In historical hiring data to ensure fairness.
Data Imbalance: With a significant number of resumes not leading to interviews or hires.
Our Solution Methodology
1. Data Analysis and Preparation:
→ Extracted data on job descriptions, resumes, candidate profiles, interview outcomes and hiring decisions from SQL Server.
→ Performed exploratory data analysis to identify key features influencing interview success.
→ Cleaned and preprocessed data, handling missing values using imputation techniques and addressing skewness with transformations.
2. Feature Engineering:
→ Mapped job descriptions, resumes, profiles using Natural Language Processing (NLP) techniques.
→ Created numerical features from candidate qualifications and experience.
→ Engineered features based on job requirements and company preferences.
3. Model Development:
→ Tested multiple machine learning and deep learning algorithms including Gradient Boosting, ANN, CNN, Transformers and Open source LLMs.
→ Selected the best-performing model based on multiple evaluation metrics such as AUC, precision, recall and F1 score.
→ Implemented techniques to detect and mitigate potential biases in the model and explainability for complete transparency.
4. Model Integration:
→ Developed a Python-based API to integrate the model with the existing SQL Server backend.
→ Implemented real-time scoring of new resume submissions.
→ Created a user-friendly interface for recruiters to view candidate scores and insights.
5. Continuous Improvement:
→ Packaged the solution and set up a feedback loop to continuously update the model with new data.
→ Implemented backtesting to compare model performance against traditional screening methods.
Value Delivered and Results
→ AUC-ROC Score of 87%: Demonstrating strong ability to distinguish between positive and negative cases across different thresholds.
→ F1 Score of 77%: Balancing precision and recall, minimizing false positives and negatives.
→ Reduced Average Time-to-Hire By ~38%: From 45 days to 28 days
→ Enhanced Recruiter Efficiency: Enabling focus on the top 20% of candidates with the highest interview success probabilities.
Tech Stack Used:
→ Python: For data processing and model development.
→ SQL Server: For data storage and retrieval. → NLTK and spaCy: For natural language processing and mapping. → Scikit-learn and Huggingface: For machine learning and deep learning. → PyTorch: For building the model development framework. → FastAPI: For API development and integration. → Docker & Kubeflow: For containerization and easy production deployment.
→ MLFlow: For real-time tracking and monitoring.
Conclusion
We successfully developed a predictive model that transformed the client's recruitment process, delivering significant improvements in efficiency and quality.
Our data-driven solution demonstrated the power of AI/ML in enhancing HR operations and achieving measurable business outcomes.
For further details or to discuss how our solutions can benefit your organization, please reach out at: info@maticsanalytics.com
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