Tesla Hiring Data Labeler 2024 | Apply Now

Location: Draper, Utah

Company: Tesla

Salary: Not Disclosed

Shift: 9:00 AM – 5:30 PM

Data Labeler Job Description

Data Labeler Roles

1. Data Annotation: Accurately labeling facts in line with distinctive hints.
2. Quality Control: Ensuring extraordinary annotations through meticulous evaluation.
3. Workflow Management: Organizing and prioritizing obligations to fulfill deadlines.
4. Communication: Collaborating with group members to solve labeling-demanding situations.
5. Adaptability: Adjusting to evolving labeling requirements and gear.

Data Labeler Responsibilities

• Labeling numerous datasets with precision and consistency.
• Performing first-class assessments to keep statistics integrity.
• Documenting labeling choices and comments for development.
• Following guidelines to ensure compliance and accuracy.
• Reporting troubles and suggesting improvements in labeling methods.
• Prioritizing tasks primarily based on assignment timelines and necessities.
• Collaborating with records scientists and engineers to optimize labeling workflows.
• Adapting to new gear and strategies for data annotation.
• Participating in schooling sessions for brand-spanking new labeling responsibilities.
• Maintaining confidentiality and protection of classified data.

Data Labeler Skills

1. Attention to detail and accuracy.
2. Critical questioning and hassle-fixing capabilities.
3. Ability to work independently and in a team.
4. Time control and organizational abilties.
5. Effective communique (written and verbal).
6. Adaptability to changing mission necessities.
7. Familiarity with facts annotation tools (e.g., Labelbox, Scale AI).
8. Basic know-how of machine mastering ideas.
9. Knowledge of statistics privacy policies (e.g., GDPR, HIPAA).
10. Basic talent in spreadsheet and records management software.

Data Labeler Education and Qualifications

• High college degree or equivalent; bachelor’s degree preferred.
• Previous revel in statistics access, annotation, or comparable roles.
• Certification in data annotation equipment or related fields (preferred).
• Familiarity with precise enterprise domain names (e.g., healthcare, automotive).
• Proficiency in applicable languages or software program gear (Python, SQL, and so on.).
• Understanding of statistical evaluation and facts interpretation.
• Ability to handle massive datasets and complicated labeling tasks.
• Strong analytical and hassle-fixing talents.
• Training in nice warranty or process development.
• Excellent documentation and file-retaining competencies.

Data Labeler Conclusion

Data labelers play a critical role in making ready datasets for devices gaining knowledge of AI programs by ensuring accurate and reliable annotations. This function requires a blend of technical competencies, attention to element, and powerful communique, making it essential in the improvement of superior technologies across numerous industries.