Personal Statement

I am an educational psychologist and researcher with six years of experience in statistical modeling and data analysis. In a recent data science project using TALIS 2024 data, I analyzed AI adoption among teachers across 55 countries and regions. I cleaned rotated survey items, separated “not administered” responses from true missing data, screened more than 500 candidate predictors, and built interpretable features such as AI benefit and AI risk belief composites. I then compared logistic regression, Lasso, Elastic Net, random forest, XGBoost, LightGBM, and gradient boosting models using school-grouped training and test splits to avoid data leakage. The final gradient boosting model predicted teacher AI adoption with a ROC-AUC of approximately 0.83. Permutation importance showed that perceived AI benefits, AI-related professional learning, and country context carried most of the predictive signal.

My dissertation focuses on measuring and improving teachers’ AI literacy, and I have also led five teacher workshops on generative AI in classroom assessment.

I am transitioning to data science and data analytics because these fields allow me to work with larger, more complex datasets and turn results into real decisions. I bring experience in Python, R, SQL, statistical modeling, machine learning, data visualization, survey data, and AI applications.

I am seeking a full-time data scientist or analytics role with a salary of around $85,000 or higher. I plan to interview in fall 2026 and begin working after completing my PhD in December 2026.

Education technology and AI-driven companies are a natural fit, but I am also open to fintech, healthcare, product analytics, and other industries where I can analyze user behavior, improve products, and solve practical problems with rigor.