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Our team is seeking a collaborative Data Annotation Specialist to support the development, evaluation, and monitoring of contact center AI. Our mission is to ensure that AI solutions deliver real business value—not just flashy features.
As a Data Annotation Specialist, you will play a critical role in creating high-quality training data and evaluating real-world AI performance to help build accurate, reliable AI systems.
This role requires expert-level English proficiency (spoken and written) and strong cognitive abilities to understand complex business problems, make informed decisions about data quality, and research correct responses in ambiguous situations. The work of our Data Annotation Specialists ensures that AI models solve real business problems and meet quality standards, helping avoid costly mistakes and improve customer experiences.
Attend team meetings and agile ceremonies to understand the business context and model objectives for each annotation project.
Apply deep comprehension and sound judgment to machine learning inputs and outputs, including:
Listening to and transcribing complex or messy audio (accents, background noise).
Scoring conversations related to technical issues for quality.
Reading transcripts to identify call drivers and user intent.
Make critical decisions regarding what constitutes high-quality data for each use case.
Proactively reach out to subject matter experts when additional context is required to ensure accuracy.
Collaborate closely with data scientists, product managers, ML engineers, architects, and data engineers.
Participate in data curation for model creation, evaluation, and ongoing monitoring.
Help design and continuously improve annotation workflows to enhance efficiency and quality.
Professional business experience.
Expert command of the English language, both written and spoken.
Strong problem-solving skills with the ability to quickly understand complex business objectives and make clear, defensible decisions about data quality.
High accountability and comfort working with limited ambiguity, including the ability to independently resolve challenges.
Critical thinking and sound judgment to weigh trade-offs and select the most appropriate data to meet business goals.
Proactive communication skills, with a willingness to engage subject matter experts when clarification is needed.
Experience with data labeling platforms such as Azure ML Data Labeling or AWS SageMaker Ground Truth.
Familiarity with cloud platforms (AWS, Azure, GCP).
Basic knowledge of Python or SQL.
Experience with NLP or conversational AI technologies.
Prior experience in data annotation or data quality roles.
Gain exposure to the full AI lifecycle, from data preparation to model monitoring.
Contribute to process improvements and workflow design.
Collaborate with cross-functional experts across engineering, product, and data science teams.
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