Spanish Data Understanding & Labelling Specialist

TikTok; Kuala Lumpur; On-site; MYR 60,000 - 90,000; Full time

Job summary

TikTok is seeking a Data Understanding Specialist (Spanish speaking) to bridge data collection, model development, and project success. You will ensure accurate application of structure to unstructured data for Spanish content across video, images, text, and more.

Fluent in Spanish and English, you will collaborate with ML engineers and labellers, define labelling rules, monitor data quality, and drive timely delivery across projects in a dynamic tech environment.

Qualifications

• Fluency in Spanish and English required for collaboration with Spanish markets and stakeholders and working language is English.
• Minimum 1 year experience with data/business analytics education/experience.
• Proficiency in data interpretation and analysis; interest in AI/ML concepts, particularly data annotation.
• Excellent verbal and written communication for clear collaboration with requestors and labellers.
• Some project management experience with ability to multitask in a data environment.
• Demonstrates learning agility and strong problem solving.

Responsibilities

• Labelling rules clarification and training.
• Quality improvement and assurance of labelled data for the Spanish market.
• Manage projects and communicate with Spanish-speaking stakeholders to ensure transparency across the data delivery pipeline.

Skills

Spanish fluency

English fluency

Data analytics experience

Data interpretation

Project management

Communication skills

Problem solving

Education

Data analytics education/experience

Tools

Excel

SQL

Python

R

Job description

TikTok is seeking a Data Understanding Specialist (Spanish speaking) to bridge data collection, model development, and project success. You will ensure accurate application of structure to unstructured data for Spanish content across video, images, text, and more.

Fluent in Spanish and English, you will collaborate with ML engineers and labellers, define labelling rules, monitor data quality, and drive timely delivery across projects in a dynamic tech environment.