I specialize in metadata quality review, data cleaning, library analytics, and human-reviewed AI-assisted workflows.
I specialize in metadata quality review, data cleaning, library analytics, and human-reviewed AI-assisted workflows.
I hold a Master’s in Library and Information Science from Simmons University and have experience working in both public and university library settings. My work combines library science, metadata review, Python-based data cleaning, exploratory analysis, and practical reporting.
My projects focus on a common problem: organizations often have valuable information trapped in inconsistent spreadsheets, legacy metadata, administrative exports, or hard-to-review records. I build workflows that clean, organize, audit, and explain that data so staff can make better decisions and improve access.
I create practical workflows that move information from messy source files to clean datasets, useful reports, and review-ready outputs.
METADATA QUALITY REVIEW
I audit digital collection and archive metadata for missing fields, malformed dates, duplicate identifiers, weak descriptions, inconsistent subjects, and other issues that affect discoverability and preservation.
DATA CLEANING & ANALYSIS
I transform messy spreadsheets, administrative logs, and exported records into clean, structured datasets that can be analyzed, queried, visualized, and used for reporting.
AI-ASSISTED REVIEW WORKFLOWS
I design careful, human-reviewed AI workflows that help flag issues, suggest improvements, and speed up review without automatically replacing professional judgment.
Certifications
Google AI Professional Certificate (2026) Google AI Essentials (2026) Google AI Prompting (2026) Claude 101 and AI Fluency Courses (2026)
My Experience
2025-PRESENT
Freelance
Information Specialist
Provide metadata auditing, information quality control, and documentation services grounded in library science, with full project management responsibility from scoping through delivery.
2022-2025
Swampscott Public Library
Reference Librarian, Adult Programmer & Cataloger
Provided in-depth reference, reader’s advisory, research assistance, technology instruction, and virtual reference support for public library patrons. Managed adult nonfiction collection development and acquisitions, selected materials based on community needs and usage trends, and created consistent selection practices.
2022
Peabody Institute Library of Danvers
Assistant Librarian
Provided reference, reader’s advisory, circulation, and interlibrary loan support in a public library setting. Assisted patrons with digital resources, library technology, and equipment, combining public-facing service with practical technical troubleshooting.
2021-2022
Boomerangs-Fenway Health
Media Librarian
Managed the thrift store’s book and media department, overseeing collection development, inventory assessment, and selection decisions for donated materials. Improved book and media sales through more intentional collection management while supporting diverse, and inclusive representation.
2018-2020
Harvard Law School Library
Library Assistant / Document Delivery Assistant
Supported faculty and student research at Harvard Law School Library through document delivery, bibliographic research, materials retrieval, scanning, and request management. Received and interpreted requests through LibAnswers, phone, and in person; located materials using HOLLIS, Harvard E-Resources, Westlaw, Lexis, OCLC/WorldCat, journal databases, and the library catalog; and processed requests through Alma and related delivery systems.
Portfolio
Archive Metadata Review Tool
A Python-based metadata quality audit tool for digital collections. Analyzes Internet Archive metadata to identify issues affecting discoverability and preservation, then generates prioritized reports for systematic review.
AI Assisted Metadata Review Tool
An AI-assisted content and metadata QA workflow that flags quality issues and produces review-ready fix suggestions at scale. An evolution of my Archive Metadata Review Tool.
Library Circulation Data Tool
An automated, Python-based data pipeline designed to ingest, clean, and analyze heavily formatted multi-table spreadsheets. This project transforms raw, layout-shifted library administrative logs into organized, query-ready datasets and compiles them into a clean web-based dashboard.
