AI-Powered 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