PROFESSIONAL EXPERIENCE
Data systems, research leadership, and production engineering.
Professional work that supports quantitative teams: SQL and ETL, reconciliation, exception management, causal-inference research, evidence-based evaluation, and reliable data acquisition.
01 / BRIGHT POWER
DATA & SYSTEMS ANALYST · FULL-TIME INTERNSHIP
Production data controls at portfolio scale.
New York, NY · Mar–Dec 2025
Constructed workflows extracting, normalizing, and validating utility data across provider portals, OCR-processed documents, and partner systems.
Wrote multi-table queries reconciling account, billing, building, and provider records across five operational systems.
Specified a validation control that reduced manual review by approximately 50% while surfacing duplicate and misconfigured records.
Resolved more than 390 data-integrity exceptions and earned a performance-based pay increase and nine-month extension.
02 / RESEARCH & EVALUATION
AHMED LAB · RUTGERS INSTITUTE FOR HEALTH
Lead Research Scientist & First Author
New Brunswick, NJ · May 2023–Apr 2024
- First-authored a peer-reviewed Wiley publication recognized as a Top Viewed Article in 2025.
- Surveyed causal-inference methodology across 150+ studies and assessed confounding, reverse causation, multiple testing, and overlapping-sample contamination.
- Directed a seven-member team through research scope, synthesis, and expert-reviewer revision.
MERCOR
Generalist & STEM Expert, AI Evaluation
Remote · 2026–Present
- Scored more than 100 artifacts against defined accuracy, completeness, contextual fit, and instruction-following criteria.
- Cited specific evidence, identified unsupported claims, and delivered structured evaluations under time-bound production requirements.
03 / EXPERIMENTAL SYSTEMS
QINGZE ZOU LABORATORY · RUTGERS SCHOOL OF ENGINEERING
Research Assistant, C++ Data Acquisition
Piscataway, NJ · Oct 2023–May 2024
Reliable measurements begin before analysis.
Developed and debugged a C++ data-acquisition system for timestamped 1 Hz sensor logging. Systematic component testing isolated hardware-software integration faults and reduced experimental data loss by approximately 50%.