Inspired by my curiosity about the factors influencing job satisfaction, this project, conducted in my statistical machine learning class, explored whether the combination of years of experience and industry growth rate predicts job satisfaction and how this relationship varies across different fields of study and occupations. Using a rigorous approach that included exploratory data analysis (EDA), causal inference through a Directed Acyclic Graph (DAG), hypothesis testing (Frequentist and Bayesian), and logistic regression via Generalized Linear Models (GLM), I examined the significance of these predictors. The frequentist analysis, using ANOVA, resulted in a p-value of 0.6455, leading us to fail to reject the null hypothesis—indicating that years of experience and industry growth do not significantly affect job satisfaction. Similarly, Bayesian analysis confirmed this result, with confidence intervals consistently including zero, reinforcing the lack of strong evidence for an effect. A posterior predictive check further suggested a poor fit between observed data and model predictions, potentially due to incorrect distributional assumptions. Recognizing these limitations, I proposed future improvements, including nonlinear modeling, alternative distributional assumptions, and Generalized Additive Models (GAM), as well as incorporating additional variables and datasets to refine the analysis. This project deepened my understanding of statistical modeling and causal inference, highlighting the complexities of real-world labor market dynamics.