Jun 2026 – Present

Data Analyst

Capacity Catalyst · Non-profit

  • Built and maintain an end-to-end ETL pipeline that deploys client-facing data visualizations to a public website via Data Studio.
  • Migrated the data layer to a Supabase (Postgres) backend to improve maintainability.
May 2026 – Aug 2026

Technical Fellow

Codepath

  • Led breakout sessions on RAG architecture, agentic workflows and applied AI for cohorts of 10, supporting 100+ students.
Aug 2025 – Dec 2025

Data Engineer Intern

Northrop Grumman Aviation

  • Built and debugged complex SQL pipelines (stored procedures, dbt) generating supplier-quality KPIs that saved 30+ hours per quarter of manual prep.
  • Designed an Azure-integrated LangChain LLM pipeline generating weekly operational reports from SQL Server manufacturing metrics, used by managers to prioritize repairs and technician allocation.
May 2025 – Aug 2025

Data Science Intern

Genentech

  • Built an unstructured data analysis pipeline using OCR and LLM prompting to convert scientific documents into structured tables for contamination monitoring, replacing manual transcription.
  • Developed interactive visualizations with seaborn, matplotlib and plotly, enabling microbiologists to interpret endotoxin datasets efficiently, including work on a high-priority Roche GLP-1/GIP receptor programme.
  • Designed a hybrid RAG pipeline combining keyword and embedding-based semantic search over SOP documents, automating a contractor compatibility review that had been manual.
Aug 2024 – May 2025

Data Science Teaching Assistant

The University of Texas at Austin

  • Data Science for Healthcare: facilitated discussions on advanced statistical methods covering survival analysis, time-to-event and longitudinal data modeling, helping 300+ students.
  • Principles of Data Science: mentored students on reproducible research, exploratory data analysis, statistical inference, hypothesis testing, regression, predictive modeling and causal inference.
Jun 2024 – Sep 2024

Data Science Intern

State of Delaware

  • Developed an ML triage system for 100K+ DNREC septic permit applications using regression and tree-based models (Logistic Regression, XGBoost, LightGBM), evaluated on precision, recall, F1 and accuracy.
  • Validated 120,000+ well water level and water use rows with SQL and DAX queries (range checks, missingness, duplicates) plus statistical outlier tests to surface anomalies.
  • Built Power BI dashboards visualizing water usage and quality across Delaware for the United States Geological Survey.