张子墨

Key Skills

Software

Internal/Proprietary Tooling

Top Subject Matter

Music/audios Domain Expertise

Gaming & Entertainment

AI Data Annotation & Taxonomy

Top Data Types

Audio

Text

Image

Top Task Types

Classification

Text Generation

Question Answering

Data Collection

Freelancer Overview

AI/Data Operations specialist with 8+ years of experience in content operations, data structuring, labeling systems, and recommendation strategy across large-scale digital platforms. Experienced in building tagging frameworks, classification systems, and AI-oriented data workflows for multi-platform products. Skilled in content taxonomy design, metadata organization, data quality optimization, and behavioral analysis using SQL and operational analytics. Hands-on experience includes content labeling, taxonomy construction, recommendation strategy optimization, workflow design, and structured data management across audio, gaming, and user behavior datasets. Strong background in cross-functional collaboration, operational execution, and scalable data workflow management.

Labeling Experience

Product Operations, Tag System Classification and Label Mapping

AudioClassification

Overhauled music library tagging structures by designing new classification hierarchies and improving label mapping accuracy. Built an end-to-end algorithmic tag-production workflow, delivering use-case and genre-based tags for diverse music data. Led error reduction initiatives for content-matching and entity association, focusing on edge cases involving artist disambiguation and tagging precision. • Produced, mapped, and evaluated scenario/genre tags for music data labeling. • Implemented error-feedback loops for ongoing label quality improvement. • Utilized artist association and audio-feature judgment for advanced tagging accuracy. • Curated content pools for regional genre/artist granularity and enhanced dataset relevance.

2023 - 2024

Lead, Audio Algorithm Data Annotation/Knowledge Graph Operations

AudioClassification

Led the operations and workflow automation for an audio accompaniment library, with a focus on data ingestion, quality assurance, and process optimization. Collaborated on audio algorithm integration and knowledge-graph data exchange to improve structured metadata accuracy and field mapping. Implemented automated processes for data preprocessing, crawl-based intake, and user-query driven enhancements, boosting efficiency and data quality to production standards. • Built and maintained automated label production pipelines for millions of audio tracks. • Coordinated audio data quality control, error analysis, and metadata field classification. • Optimized structured search, metadata mapping, and audio dataset annotation workflows. • Worked closely with cross-functional teams to support ongoing AI algorithm training and data validation.

2019 - 2023

Education

Work History