Project detail
People Analytics with PyMC
Exploration of skill-based organization and probabilistic analysis for talent decisions and capability gaps.
Highlights
- Probabilistic skill and talent gap modeling
- Application to skill-based organization
- Focus on development and assignment decisions
- Useful framework for workforce planning
No public repository or demo link is published for this project yet.
Architecture
Applied PyMC guide for People Analytics with Bayesian models oriented toward skill inventory, future gaps, and development recommendations
Challenges
- Representing skills dynamically rather than statically
- Estimating future gaps with uncertainty
- Translating analysis into operational talent decisions
Outcome
A practical foundation for using PyMC in more flexible, predictive talent strategies aligned with real skills
Overview
Problem
Traditional organizations model work by job titles, which hides talent and makes it difficult to respond when needs change.
Approach
I developed a People Analytics framework based on skills to infer capabilities, detect gaps, and prioritize development using Bayesian models.
Outcome
This work demonstrates how PyMC can support more agile and useful talent decisions compared to a rigid scheme based solely on job titles.