Yusra Rasool

Data Engineering Intern

Nucor

This role required turning a loosely structured industrial problem into usable pipelines, quality checks, governance practices, and analyst-friendly outputs that engineers and stakeholders could act on.

Ambiguous industrial telemetry problem. Defined workable structure. Improved governance, reporting, and cost visibility. Helped engineers move from vague signals to buildable and testable outputs.

• Worked through an ambiguous telemetry problem space and helped shape buildable solutions across Azure, Databricks, SQL Server, Microsoft Fabric, and Power BI.
• Analyzed Databricks compute cost across Nucor projects by category and usage pattern using Databricks Unity Catalog so spend was easier to explain, compare, and manage.
• Created structure around 100+ business technology assets through Informatica marketplace and governance work for finance data and HR stakeholders.
• Improved data quality and observability across high-frequency sensor pipelines before downstream modeling or decision support.
• Collaborated around anomaly detection and driver explanation so technical findings were easier to interpret, validate, and iterate on.
• Supported proof-of-concept and pilot-style ML workflows that reached 89% validated breakout forecast accuracy.

Undergraduate AI Researcher

TKAI Lab / University of South Florida

Research taught me how to work without fixed answers: form hypotheses, test alternatives, document learnings, and keep iterating until the signal is clear.

Long-form research experience built the habit of questioning assumptions, testing alternatives, documenting learnings, and iterating until the reasoning is defensible.

• Defined and tested alternatives to standard training approaches, including Local Representation Alignment.
• Worked iteratively through experimental design, implementation, validation, and comparison under constrained compute settings.
• Documented learnings, assumptions, and results in ways that supported continuity and future experimentation.
• Built a strong research habit around questioning assumptions, comparing alternatives, and making technical findings easier to explain.

Compliance Team Lead

USF Business and Finance

This role sharpened the business-systems side of my work: document the logic, clarify the process, test edge cases, and make sure the workflow holds up under pressure.

Finance and compliance work forced precision. I had to keep process logic clear, validate edge cases, use JIRA and automation well, and protect accuracy under audit pressure.

• Maintained responsibility across 156 accounts with zero regulatory failures and zero audit-period errors.
• Led audit and reconciliation workflows through sensitive financial and compliance constraints with finance data accuracy as a hard requirement.
• Used JIRA, process tracking, and Python automation to reduce manual effort, improve repeatability, and tighten validation on invoice and vendor processes.
• Captured process logic and edge cases clearly enough for continuity, verification, and future handoff.