Summary: We are seeking a meticulous and highly detail-oriented Data Labeling Specialist to join our AI/ML team. In this critical role, you will be responsible for accurately annotating and categorizing various types of raw data, transforming it into high-quality training datasets that power our machine learning models. Your work directly contributes to the intelligence and performance of our AI-driven products and services.
Key Responsibilities:
· Accurately and consistently label, tag, or annotate large datasets (e.g., images, videos, audio files, text documents) according to specific project guidelines and instructions.
· Utilize various annotation tools and platforms including some internally developed tool (e.g., bounding boxes, polygons, keypoint annotation, semantic segmentation, transcription, sentiment tagging, named entity recognition).
· Perform quality control checks on your own work and, occasionally, review annotations made by peers to ensure high data quality and consistency.
· Identify and report any inconsistencies, ambiguities, or issues with the annotation guidelines or raw data to project leads and data scientists.
· Collaborate effectively with Data Scientists, AI/Machine Learning Engineers, and Project Managers to understand project requirements and refine annotation processes.
· Maintain detailed records of annotated data and adhere to strict project deadlines and timelines.
· Continuously learn and adapt to new annotation techniques, tools, and project requirements.
· Adhere to data privacy and security protocols when handling sensitive information.
Qualifications:
· Experience:
o Bachelor's degree in computer science, data science, data analytics or any data related major
o 3+ years of experience in data annotation or a related field (e.g., data entry, transcription, content moderation).
o Experience with specific annotation types (e.g., image annotation, text labeling, audio transcription) is a plus.
o Knowledge of mobile phone or similar electronic device and apps in the phones is a plus
