Data Analytics Intern, later Data Analytics Consultant (June 2024 – Sept 2026), building predictive models, validated data pipelines, and automation tools across antimicrobial research and clinical trial workflows.
Project details and visuals are intentionally generalized to protect confidential and proprietary information.
Developed and improved automated tools for a high-throughput optical checkerboard assay used in antimicrobial combination research. Automated calculations, interaction classification, quality-control checks, and visualization of assay results, reducing repetitive manual analysis and making results easier to review.
Conceptual diagram: synthetic example, not actual assay data
Built predictive models for R&D and clinical trial data supporting pre-surgical infection prevention research, using analytical pipelines in Python, R, and Excel to process clinical lab data.
Built tools to automate the creation, cleaning, transformation, validation, and formatting of clinical research datasets for downstream BioTrial workflows, focused on producing consistent, analysis-ready data while reducing repetitive manual processing.
Engineered reproducible, validated data cleaning and transformation workflows across 4 types of clinical and laboratory data, including multi-run observational datasets. These reusable workflows cover data cleaning, transformation, statistical analysis, and visualization, designed to make scientific data easier to analyze, validate, and communicate.
Designed automated laboratory inventory and batch-record management workflows for sensitive data, reducing manual work and improve consistency and traceability.
Created clear visualizations and analytical summaries that translated complex laboratory and clinical data into interpretable outputs for scientists and other stakeholders.