AI-Driven Workforce Allocation

Led development of innovative AI-driven workforce planning platform that transformed talent management processes and delivered significant cost savings through intelligent automation and predictive analytics.

Technical Environment: Python, SQL, Tableau, Scikit-learn, TensorFlow, NLTK

·   Machine Learning: LSTM Networks for Time-Series Forecasting, XGBoost for Attrition Prediction

·   NLP: BERT-based Models for Skill Extraction and Classification

·   Optimization: Linear Programming and Genetic Algorithms for Resource Allocation

Key Responsibilities:

·   Architected end-to-end AI solution for workforce planning and talent allocation using microservices architecture

·   Developed and deployed LSTM neural networks for time-series forecasting of headcount needs

·   Implemented XGBoost models for employee attrition prediction with 87% accuracy

·   Designed BERT-based NLP pipeline for automated skill extraction from resumes and job descriptions

·   Created genetic algorithm-based optimization system for optimal resource allocation

·   Built real-time predictive analytics dashboards integrating multiple ML model outputs

·   Collaborated with HR leadership to define requirements and ensure solution alignment

Key Achievements:

·   Reduced organizational hiring costs by 20% through optimized talent allocation strategies

·   Improved headcount forecasting accuracy by 40% using advanced time-series modeling

·   Achieved 85% accuracy in predicting future talent needs and skill gap identification

·   Increased internal mobility by 30% through AI-powered skill mapping system

·   Reduced manual analysis time by 60% through process automation

·   Developed comprehensive predictive analytics dashboard integrating multiple data sources

·   Successfully deployed solution across organization, establishing new standard for data- driven workforce planning