Naomi Harkins

Quality assurance · data validation · user-centered review

I look for the details that decide whether a result, report, or digital experience can be trusted.

I am a recent Astronomy/Astrophysics graduate with experience checking data, investigating unexpected results, auditing public-facing information, and explaining what needs to happen next. I am working toward a career in quality engineering and accessibility.

Review selected projects View resume Email Naomi

About

A quality mindset built through research and review

Research taught me not to force a clean story when the evidence is messy. In my projects, I keep sample counts visible, compare expected and actual outputs, trace inconsistencies back to their source, flag limitations, and write recommendations that another person can act on.

Selected work

Completed projects that show how I investigate quality

These case studies are based on completed internship, research, and course work. The public versions use cleaned summaries and do not claim direct assistive-technology testing or other experience that has not been completed.

Completion rates across seven audited public business profiles.

Business Profile QA Audit

A structured consistency review that documented 25 missing, inaccurate, or broken information checks across seven public platforms.

QA auditSource verificationPrioritization
View the Business Profile QA Audit
Six astronomy charts summarized by the main readability problem found in each one.

Scientific Chart Readability Review

Reviewed six astronomy charts and explained where crowded points, low contrast, oversized markers, or unclear labels made the data harder to understand.

Chart reviewReadabilityRecommendations
View the Scientific Chart Readability Review
Spectroscopic completeness for the SDSS galaxy sample.

SDSS Data Quality

Python and SQL analysis of 164,072 galaxy records, missing spectra, sample filtering, and reproducible scientific workflows.

PythonSQLData validation
View the SDSS Data Quality case study
Comparison of the H and M email test and Warby Parker landing-page experiment.

A/B Testing Decisions

One statistically significant experiment and one non-significant experiment, with recommendations tied to evidence rather than direction alone.

StatisticsExcelDecision quality
View the A/B Testing Decisions case study
Regional renewable-energy percentage comparison.

Renewable Energy Dashboard

Tableau dashboard logic comparing renewable share, energy mix, and net production to support a regional recommendation.

TableauDashboard checksDecision support
View the Renewable Energy Dashboard case study

Skills

Technical tools and working strengths

Quality

Consistency audits, expected-versus-actual documentation, data validation, anomaly investigation, source verification, issue prioritization, and reproducible workflows.

Technical

Python, SQL, UNIX/command line, Jupyter Notebook, Excel, Tableau, SDSS CasJobs, IRAF, A/B testing, hypothesis testing, and dashboard validation.

Communication

Visual usability review, technical reports, data storytelling, research discussions, cross-functional collaboration, and bilingual English/Spanish communication.

Site accessibility

The portfolio is designed to be straightforward to navigate

The site uses semantic landmarks, a skip link, visible keyboard focus, descriptive links, high-contrast text, meaningful image alternatives, responsive reflow, and no JavaScript requirement. This is not a formal accessibility certification; current limitations are documented in the accessibility notes.