Job Description
Role Overview :
We are looking for a Data Quality Labeling Analyst to join our large retail client's team. They are focusing on the development of Machine Learning (ML) models that analyze and standardize product characteristics.
This role involves assessing and labeling data, particularly focused on AI-driven categorization of retail products. Your primary responsibilities will include meticulously labeling products where automated systems like Gemini fall short, ensuring accurate categorization of items such as Bakery, Fruits, Snacks, etc.
This position requires a keen eye for detail and strong critical thinking skills. This role demands meticulous attention to detail and critical thinking.
Responsibilities :
Review of Annotated Data : Review of annotated ground truth data which includes evaluating the accuracy of labels generated through Azure ChatGPT and Google Gemini LLMs.
The accuracy of generated labels will be evaluated against provided definitions for each label.
- Manual Review of Model Outputs for product characteristic s : Conduct thorough manual reviews using Excel to analyze ML model outputs using the provided documentation that outlines the criteria for model performance as well as detailed definitions for each product characteristic.
- Assessing and labeling data to improve the accuracy of AI-driven product categorization.
- Reviewing and refining labeling processes to enhance efficiency and precision.
- Collaborating with cross-functional teams to ensure data labeling aligns with project objectives.
- Testing and reviewing results from AI models to validate their accuracy.
- Providing guidance and support to additional QA resources as needed.
Qualifications :
- 1-3 years of experience in a relevant role, preferably with exposure to ML model development or similar technical fields.
- Excellent communication skills, both written and verbal, with the ability to convey complex information clearly and persuasively.
- Strong analytical and critical thinking skills, with a keen eye for detail and a commitment to accuracy.
- Proficiency in reviewing and annotating data, with a basic understanding of ML model development principles.
- Strong collaboration skills, with the ability to work effectively with development teams and stakeholders.
- A proactive approach to problem-solving, willing to challenge test scenarios and suggest improvements.
- Excellent communication skills necessary for reviewing product information against business requirements and communicating effectively within the team.
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