Beatrice M.

Key Skills

Software

Appen

Clickworker

Data Annotation Tech

Labelbox

Mercor

Mindrift

OneForma

Remotasks

Snorkel AI

Telus

Top Subject Matter

Japanese language data labeling and NLP training (sentiment/intent/semantic tasks)

Japanese–English localization QA for linguistic datasets

Legal Services & Contract Review

Top Data Types

Text

Image

Video

Top Task Types

Evaluation/Rating

Transcription

Text Summarization

Text Generation

Object Detection

Segmentation

Classification

Polygon

Entity (NER) Classification

Bounding Box

Point/Key Point

Polyline

Cuboid

RLHF

Fine-tuning

Red Teaming

Question Answering

Computer Programming/Coding

Data Collection

Function Calling

Prompt + Response Writing (SFT)

Freelancer Overview

AI Data Annotator – Japanese Language (Freelance, Remote – U.S.). Brings 5+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include N and A. Education includes Bachelor of Arts, University of Nairobi (2021) and Certificate, Coursera (2022). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Translation.

Labeling Experience

AI Data Annotator – Japanese Language (Freelance, Remote – U.S.)

Text

Annotated Japanese-language datasets to support AI and NLP model training and downstream content understanding tasks. Evaluated AI-generated text for fluency, tone, and cultural accuracy using guideline-driven criteria. Performed sentiment analysis, intent classification, and semantic labeling to generate structured labels for model use. • Fluency, tone, and cultural accuracy checks • Sentiment analysis and intent classification • Semantic labeling and guideline consistency • Confidentiality and quality benchmark adherence

2023 - Present

Language Quality Reviewer – Japanese « English (Contract, Remote)

Text

Reviewed bilingual Japanese–English datasets to verify linguistic correctness and localization quality. Corrected grammar, tone, and cultural mismatches to improve dataset usability and consistency. Supported QA workflows and contributed to guideline improvements for more reliable labeling outcomes. • Grammar and tone correction • Cultural mismatch detection and fixes • QA workflow support and guideline refinement • Bilingual localization accuracy review

2022 - 2023

Education

Work History