Muda Y.

Key Skills

Software

No software listed

Top Subject Matter

Legal Services & Contract Review

Regulatory Compliance & Risk Analysis

Legal Research & Document Analysis

Top Data Types

Text

Document

Top Task Types

Segmentation

Freelancer Overview

I have hands-on experience producing and validating the structured, ground-truth data that AI training and evaluation depend on. In my game-mathematics work I designed and generated large labeled test datasets and ran simulations to validate system outputs against expected reference values, systematically flagging mismatches. This is the same core discipline as evaluating model outputs against a rubric and labeling them correct or incorrect. Across both work and study—whether for writing documents, coding, or data visualization—I regularly tailor prompts to different needs to make AI outputs more effective. Alongside this, I author detailed specifications and rule tables, directly analogous to writing and following precise annotation guidelines: defining unambiguous rules, handling edge cases, and keeping labels consistent across a large body of items. What sets me apart is the combination of high-volume accuracy under detailed guidelines and trilingual fluency in Chinese, Japanese, and English, making me effective on multilingual annotation and evaluation tasks. I'm comfortable working through ambiguous edge cases, closely following labeling instructions while flagging where they're unclear or conflicting, and catching subtle errors that automated checks miss—whether the task is classification, response-quality ranking, prompt iteration, fact-checking, or producing structured outputs at scale.

Labeling Experience

Stock Market Sentiment Classification from Social Media Posts

TextClassification

I have hands-on experience labeling text data for AI training, with a focus on sentiment analysis. In a stock-market sentiment project, I worked with social-media posts (Twitter/X) related to specific tickers and markets, tagging sentiment-bearing words and classifying each post as positive, negative, or neutral. This involved applying a consistent labeling scheme across a large volume of posts, handling the messy realities of real-world text—sarcasm, mixed signals, finance-specific slang and tickers, and ambiguous cases where a post leaned bullish or bearish without stating it outright—and resolving them against clear rules. The labeled output then fed into downstream analysis, so accuracy and consistency directly affected the quality of the results.

2021 - 2023

Education

Work History