Required Skills:

📍Data Annotation and Labeling: Extensive experience with data labeling tools and techniques, with the ability to create high-quality, structured datasets. 📍Data Cleaning and Preprocessing: Strong understanding of data cleaning methods to prepare datasets for model training. You can handle structured and unstructured data formats. 📍Analytical Skills and Quality Assurance: Ability to assess data quality and accuracy through quality assurance processes. You can troubleshoot and refine data to enhance model performance. 📍Collaboration and Communication: Strong communication skills to work with cross-functional teams and explain data requirements. You’re detail-oriented and organized in managing datasets. 📍Technical Skills: Proficiency in data manipulation using tools like Python or SQL. Familiarity with data labeling platforms (e.g., Labelbox, Amazon SageMaker Ground Truth, or Supervisely) is beneficial.

Educational Requirements:

📍Bachelor’s or Master’s degree in Data Science, Computer Science, Information Management, or a related field. Equivalent experience in data annotation, AI training, or machine learning support may be considered. 📍Certifications in data science or machine learning fundamentals are advantageous but not required.