Revolutionizing Employee Engagement with Explainable AI
Advanced ML (XGBoost, LightGBM, SHAP, LIME) for 25 years of HR data: actionable insights, targeted retention, clear dashboards, transparent recommendations.
HR Analytics Impact
Advanced engagement scoring and explainable ML for HR decision-making
Project Details
Qualintra
HR Consulting – Engagement Scoring & Model Explainability
2024
Mission
Lead Senior Data Scientist – Machine Learning Modeling, Explainability & HR Recommendations
HR Data Science – Engagement Scoring & Model Explainability
Data Science Mission / Paris (remote/hybrid)
Development of a machine learning pipeline to explain and optimize HR engagement scoring (200 questions, 25 years of multi-client data). Migration from statistical model (GLM) to advanced algorithms (XGBoost, LightGBM, Random Forest). Advanced data cleaning and imputation (66% missing values), outlier detection, multicollinearity management (correlation, PCA, regularization).
Contexte & Environnement
Qualintra, a leader in HR consulting, faced major challenges in transforming 25 years of rich HR data into actionable insights. The complexity of multi-client engagement scoring, combined with high data sparsity (66% missing values) and the need for transparent, explainable recommendations, required advanced machine learning models with strong interpretability features.
1 Lead Senior Data Scientist / 2 Data Scientists / 1 ML Engineer / 1 HR Analyst
Python with PySpark, PowerBI/Tableau dashboards
Objectifs Cles
Technologies & Infrastructure
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