Data Labeling Analyst - Speech & Voice AI

Welocalize•San Francisco, CA

18h

About The Position

The Data Labeling Analyst works on speech and voice AI projects, supporting machine learning model updates and ensuring model efficacy. The role involves managing and annotating training and test data, initiating model training through internal tools and command-line interfaces, evaluating trained models, and conducting quality analysis. The analyst also applies foundational knowledge of machine learning, data annotation, quality assurance, natural language processing, and linguistics. The work primarily focuses on English US datasets.

Requirements

Ability to work in a fast-paced, collaborative environment.
Excellent communication skills.
Familiarity with command-line tools and interfaces.
Strong analytical skills and ability to identify patterns and anomalies.

Nice To Haves

Bachelor's degree in Computer Science, Data Science, Linguistics, Computational Linguistics, or a related field.
Familiarity with translation or multilingual datasets for potential future projects.

Responsibilities

Update training and test model databases with new or amended synthetic textual and image data.
Modify and refine machine-learning data creation, annotation, and rating guidelines.
Initiate model training processes using internal tools and command-line interfaces.
Evaluate trained model performance and assess efficacy and readiness for deployment.
Design and develop test and training datasets according to criteria provided by the project manager and other full-time employees.
Manage data efficiently and preserve its integrity throughout the workflow.
Perform data relevance tasks to ensure datasets align with project goals.
Annotate data accurately according to established guidelines.
Conduct manual quality analysis of model results.
Identify error patterns and report anomalies for further investigation.
Prepare detailed reports on utterance quality, LLM evaluation, ASR bug tracking, and customer pain points for review by the User Experience Research team.
Implement basic quality-control measures and ensure processed data is reliable.
Use intermediate data-analysis techniques to extract insights and inform decision-making.
Arbitrate discrepancies to ensure consistent data quality.
Apply basic natural language processing and linguistics knowledge to data-processing tasks.
Ensure linguistic accuracy in processed and annotated data.