Messy training data
Training data accumulates inconsistencies over time. I help clean, review, and improve datasets while identifying the processes that caused quality issues in the first place.
AI Data Operations • Human Judgment Systems
Hi, I'm Danielle.
I like untangling messy data.
Some people enjoy building models. I enjoy figuring out why the data behind them isn't working.
I work across the entire AI data lifecycle-from annotating and reviewing data to designing the workflows,
taxonomies, and quality systems that make reliable AI possible.
Experience
Community
Problems I Solve
Inconsistent human judgment
Different reviewers make different decisions. I design clearer guidelines, calibration processes, and QA loops that improve consistency.
Taxonomies that no longer scale
Categories evolve organically until they become difficult to maintain. I redesign taxonomy structures that support both people and machine learning.
Weak evaluation pipelines
Models improve only when evaluation is reliable. I build practical evaluation workflows that produce trustworthy signals.
Annotation workflows under pressure
As teams grow, quality often drops. I help improve reviewer workflows, onboarding, and quality control without sacrificing speed.
Hidden quality issues
Some quality problems aren't obvious until they're expensive. I investigate recurring patterns, review data, and identify where failures originate.
Found yourself with a messy data problem?
Those are my favorite.
Tell me what you're building, where things feel messy, and let's see if I can help bring some structure to it.