AIML - Senior Quality Program Manager, Data Operations
Job Description
Summary
Imagine what you could do here? At Apple, extraordinary ideas have a way of becoming great products, services and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish!
Description
As a Senior Quality Program Manager, you will orchestrate initiatives to ensure the highest level of data quality for our AIML training. You will collaborate closely with Engineers, Data Scientists, and Machine Learning Engineers to develop, implement, and maintain tools and processes that evaluate and improve the quality of our data. Your insights and strategies will directly influence the accuracy and reliability of our AIML models.
You will be responsible for the following: Design, develop, and supervise the implementation of processes and tools to assess the quality of data used in AIML model training. Build metrics and KPIs to evaluate data accuracy, consistency, and relevance. Work with Engineering teams to develop automated logic checks that will identify inconsistencies and potential issues in the training data. Lead the integration of quality processes into existing data pipelines. Collaborate with Data Scientists to scrutinize annotation data and develop strategies for continuous data quality improvement. Provide feedback and ensure alignment of data quality with annotation guidelines. Engage with Machine Learning Engineers to determine how data quality variations influence AIML model performance. Recommend adjustments to data collection, preprocessing, and utilization based on model performance analysis. Stay on top of the latest trends and advancements in data quality management. Recommend and implement improvements to our quality processes, tools, and methodologies based on industry best practices.
This is a highly reciprocal position that requires working with Engineering, Quality, Training and Production Ops to deliver world-class solutions. And most of all, you are able to manage and lead change effectively while maintaining Apple culture and standards. Interpersonal skills and technical product knowledge and expertise are essential for this role. Applicants should thrive in dynamic, fast paced situations and welcome critical-thinking opportunities.
You will be responsible for the following: Design, develop, and supervise the implementation of processes and tools to assess the quality of data used in AIML model training. Build metrics and KPIs to evaluate data accuracy, consistency, and relevance. Work with Engineering teams to develop automated logic checks that will identify inconsistencies and potential issues in the training data. Lead the integration of quality processes into existing data pipelines. Collaborate with Data Scientists to scrutinize annotation data and develop strategies for continuous data quality improvement. Provide feedback and ensure alignment of data quality with annotation guidelines. Engage with Machine Learning Engineers to determine how data quality variations influence AIML model performance. Recommend adjustments to data collection, preprocessing, and utilization based on model performance analysis. Stay on top of the latest trends and advancements in data quality management. Recommend and implement improvements to our quality processes, tools, and methodologies based on industry best practices.
This is a highly reciprocal position that requires working with Engineering, Quality, Training and Production Ops to deliver world-class solutions. And most of all, you are able to manage and lead change effectively while maintaining Apple culture and standards. Interpersonal skills and technical product knowledge and expertise are essential for this role. Applicants should thrive in dynamic, fast paced situations and welcome critical-thinking opportunities.
Minimum Qualifications
- 5+ years of experience in data quality management.
- Demonstrated experience in project management and cross-functional collaboration.
- Outstanding analytical, problem-solving, and organizational skills.
- Solid ability to think strategically about business, product, and technical challenges.
- Work independently, prioritize tasks, and manage multiple projects simultaneously in a fast-paced environment.
- Strong verbal and written communications skills with the ability to work effectively across internal and external organizations and virtual teams.
Preferred Qualifications
- Solid understanding of quality assurance methodologies and machine learning principles, especially in the context of NLP and LLMs.
- Master's degree or higher in Computer Science, Data Science, Engineering, or related field.
- Proficiency in scripting or programming languages commonly used in data analysis (e.g., Python, R).
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