Develop and evaluate AI training datasets across multiple domains, designing structured prompts and validation frameworks to improve model performance and accuracy. Conduct systematic ranking and evaluation of AI-generated responses using quantitative metrics, identifying patterns in model behavior and providing data-driven recommendations for optimization. Perform quality assurance testing on machine learning models, detecting biases, inaccuracies, and edge cases through statistical analysis and validation protocols. Analyze model performance data to identify trends, patterns, and areas for improvement, generating insights that inform training data optimization strategies. Document evaluation methodologies, test results, and optimization recommendations in technical reports for AI development teams

Analyzed accounts receivable data for global fitness education company, identifying trends in payment patterns, delinquency rates, and client behavior to support data-driven financial decision-making. Managed and maintained financial databases, ensuring data accuracy and integrity across 1,000+ client accounts through systematic validation and quality control processes. Developed analytical insights from account activity data to predict payment likelihood and inform personalized communication strategies, improving account resolution rates. Processed and reconciled payment data with attention to detail, using a CRM environment for data tracking, reporting, and trend visualization. Collaborated with cross-functional teams in multicultural environment, communicating data insights to non-technical stakeholders and supporting strategic initiatives. Applied problem-solving and analytical skills to resolve complex account discrepancies, demonstrating ability to work with ambiguous data and deliver actionable solutions.

Developed and validated analytical methods for nanoparticle characterization, processing 200+ samples and generating comprehensive datasets using Python for statistical analysis and visualization. Built data analysis pipelines with ImageJ and Excel, reducing processing time by 60% and improving reproducibility of spectroscopic analysis (UV/Vis, FTIR). Designed and executed multi-factorial experiments optimizing extraction protocols, analyzing results to identify key parameters affecting yield (increased 35%) through systematic data-driven approach. Conducted 6-month stability studies monitoring degradation and performance metrics, performing trend analysis and generating shelf-life recommendations through systematic data evaluation. Prepared technical reports and analytical protocols following data integrity principles (ALCOA+), translating complex experimental data into actionable insights.

Developed MATLAB scripts for simulations of microfluidic channels, to contrast results from image analysis and data extraction from microscopy images. Implementing quality control checks and statistical validation. Managed experimental databases ensuring data integrity and compliance with regulatory standards in GMP-regulated cleanroom environment. Performed data analysis contributing insights that improved experimental reproducibility by 25%, demonstrating ability to derive actionable conclusions from complex datasets.

Conducted independent research involving experimental design, data collection, and statistical analysis for 8-month thesis study. Performed quantitative analysis of spectroscopic datasets (100+ samples) using Origin and Python, implementing systematic data processing workflows. Prepared comprehensive thesis (60+ pages) with statistical analysis and data visualization, receiving Top 10% distinction.