About The Role:

The NLP Data Annotation Specialist will play a crucial role in supporting various Machine Learning (ML) and Natural Language Processing (NLP) projects by accurately labeling data across a range of use cases, including Intent Detection, Summarization, Knowledge Retrieval, and Generative AI. This role requires collaboration with Data Scientists, Engineers, Conversation Designers and other stakeholders to ensure high-quality data annotations and model feedback that drive model performance and customer satisfaction.

Responsibilities:

• Data Labeling: Accurately label data for various ML/NLP tasks, including intent identification, entity recognition, text summarization, and generative responses, ensuring consistency and high quality.
• Use Case Prioritization: Assist in prioritizing labeling tasks based on project needs and customer requirements, working closely with senior team members.
• Collaboration: Work with cross-functional teams, including ML engineers, product managers, and conversation designers, to understand labeling requirements and deliver on project goals.
• Quality Assurance: Conduct quality checks on labeled data, refining annotations based on feedback, and iterating to improve labeling guidelines.
• Customer Interaction: Participate in customer-facing meetings to understand business requirements, and incorporate feedback into the labeling process.
• Documentation: Maintain detailed documentation of labeling processes, guidelines, and decisions to ensure consistency and scalability in future projects.
• Continuous Improvement: Suggest and implement improvements to the labeling process, tools, and workflows based on best practices and emerging trends in the field.

Qualifications we value:

• NLP/ML Knowledge: Familiarity with NLP and ML concepts, particularly in the context of data labeling for tasks like intent detection, summarization, and generative AI.
• Attention to Detail: Strong ability to focus on intricate details while maintaining high accuracy in data labeling.
• Collaboration: Ability to work effectively in a cross-functional team, collaborating closely with engineers, product managers, and other stakeholders.
• Critical Thinking: Analytical skills to identify patterns in data and make informed decisions on labeling strategies.
• Communication: Strong verbal and written communication skills to articulate labeling decisions and collaborate with internal and external stakeholders.
• Tool Proficiency: Experience with labeling tools and platforms, with a willingness to learn new tools as needed.

Preferred Skills:

• Multilingual Proficiency: Ability to accurately label data in a non-English language, such as Spanish, French, or German, to support diverse use cases across various linguistic contexts (strongly preferred).
• Virtual Agent Tooling: Familiarity with Dialogflow CX or other virtual agent development software.
• Copywriting & Regex: Understanding of optimal copywriting practices for customer-facing content, prompt writing for virtual agents, and regular expressions for model improvements.
• Contact Center Experience: Experience working in or with contact centers, with an understanding of common challenges and requirements.

Conclusion:

Compensation for this position includes a base salary, equity, and a variety of benefits. Actual base salaries will be based on candidate-specific factors, including experience, skillset, and location, and local minimum pay requirements as applicable. We are actively hiring for this role in the US. Your recruiter can provide further details.

Base Salary: 50,000 - 60,000 USD + bonus (10%) + equity