Marselinus Alberto Moa

Evaluated and ranked AI-generated responses based on relevance, clarity, and factual accuracy to improve model performance. Designed and refined prompts to optimize AI outputs, ensuring alignment with project requirements and user expectations.

Evaluated search engine ads using a web-based evaluating tool. Demonstrated proficiency in online research and effectively managed diverse workloads in a remote work environment.

Assessed and improved search engine relevance by analyzing search results and enhancing overall search quality. Conducted image labeling and annotation tasks to support machine learning models.

Grade: 3.38/4.00

Bachelor of Pharmacy (Apothecary Profession) with a GPA of 3.38/4.00. Concentrations included Pharmaceutical Science and Technology, with relevant coursework in Computer-Aided Theoretical Design, Biochanin A Derivative as Receptor Ligands Estrogen-alpha, R programming language, PyMOL, Organic Chemistry, and Drug Discovery.

Completed a Data Scientist Bootcamp with a final project on Machine Learning Model for Temperature Prediction for Steelproof Steel Mill, utilizing Python, Data Cleaning, Data Visualization, Feature Engineering, and Supervised Machine Learning with Scikit-learn.

Completed a course in Supervised Machine Learning: Regression and Classification, focusing on Advanced Learning Algorithms including Mathematics, Linear Regression Algorithm, Gradient Descent, Logistic Regression Algorithm, Artificial Neural Networks, Model Development, Tree Ensembles, and XGBoost.