As a Business Analyst & Data Annotator, you will play a crucial role in gathering and analyzing business requirements, acting as a bridge between stakeholder needs and technical teams. You will also handle the data annotation process, ensuring the production of high-quality, accurately labeled datasets necessary for training machine learning models.
This role involves close collaboration with ML engineers, data scientists, and business teams to ensure that data aligns with project goals. Your work will center on translating complex business needs and technical specifications into clear instructions, managing data labeling workflows, and maintaining data quality standards.

REQUIREMENTS:

• At least 1 year of experience in the relevant role;
• Excellent English language skills (B2 level or higher, ideally C1), especially when working with reports containing complex terminology;
• Strong analytical skills and an understanding of business workflows;
• Attention to detail and ability to create clear instructions and guidelines for annotation teams;
• Understanding of data privacy, security standards, and compliance requirements.

• Develop and implement detailed guidelines and instructions for data labeling and annotation to ensure consistency and accuracy across datasets;
• Review and validate labeled data, providing constructive feedback to annotation teams to improve data quality and adherence to project standards;
• Collaborate with data scientists and ML engineers to prepare, organize, and support the creation of high-quality annotated datasets for model training;
• Manage the annotation workflow, prioritize tasks, and track progress to ensure timely delivery of labeled data;
• Maintain high standards of data privacy, security, and compliance throughout all annotation processes;
• Gather and analyze business requirements, workflows, and terminology to understand data needs and improve annotation processes;
• Facilitate communication between technical teams and stakeholders by translating complex technical or domain-specific language into clear, accessible instructions and explanations;
• Offer insights into business processes that could benefit from automation or ML solutions, supporting the design and implementation of such projects;
• Support continuous improvement of data annotation guidelines, workflows, and overall business analysis practices to enhance efficiency and data quality.