Modeling Career Mobility and Attrition Using Markov Chains and Survival Analysis

This study presents a stochastic modeling framework for analyzing career progression and employee retention through three complementary methodologies: discrete-time Markov chains, survival analysis, and multivariate logistic regression. Using longitudinal human resources data from a multinational technology corporation, we model transitions between four hierarchical states: junior, confirmed, manager, and exit; while addressing data censoring through robust imputation techniques. The transition matrix estimation incorporates bootstrap confidence intervals and regularization to ensure statistical reliability. Key findings reveal substantial inertia in managerial roles, with a 75% probability of remaining unchanged between periods. A critical salary threshold emerges at 6,500 euros monthly, beyond which attrition risk decreases by 60%. Survival analysis identifies elevated exit probabilities between two and five years of tenure, suggesting strategic intervention windows. While promotions reduce attrition odds non-linearly, most effectively at junior levels, their protective effect diminishes by 34% for managerial positions. Multivariate models confirm job satisfaction and promotion history as dominant retention predictors, with respective odds ratios of 0.73 and 0.66. The framework demonstrates strong predictive validity (adjusted R-squared = 0.85) and conforms to normality assumptions (Shapiro-Wilk p-value = 0.15). Methodological constraints include potential survivorship bias in tenure data and discrete time intervals masking shortterm transitions. Practical applications enable organizations to simulate policy impacts, optimize retention budgets, and design personalized career pathways. These contributions advance both academic research in organizational behavior and evidence-based human capital management strategies. Full computational implementations are provided for reproducibility and practical adaptation.