Apple • Cupertino, CA 95015
Job #2684390578
Data Quality Analyst - SIML, ISE
Cupertino,California,United States
Software and Services
Would you like to contribute to generative AI and transform how people interact with AI technologies? Do you believe Machine Learning and AI can change the world? We truly believe it can! We are the Data Team of the System Intelligence and Machine Learning (SIML) group within the software engineering organization at Apple. We are responsible for building high-quality ML datasets at scale, used to train ML models that power AI-centric features for many Apple products (iPhone, iPad, Mac, Apple Watch, and even AirPods). Such features go from the smart wallpaper on your iPhone Lock Screen to the models that highlight the faces of your loved ones in your Photos app to input experiences (e.g., autocorrect, next-word prediction, handwriting recognition). We are looking for a Quality Analyst who demonstrates exceptional attention to detail and a deep focus on quality. Someone passionate about Apple products and values, who loves collaborating and working with data ops at scale, and who is committed to the hard work necessary to improve data quality for our R&D partners continuously. We invite you to join us at this exciting time! Grow fast and positively impact multiple critical features on your first day at Apple!
Key Qualifications
2+ years of experience as a Quality Analyst with exposure to data annotation, and/or data collection quality evaluation.
Data analytics experience with the ability to track, analyze, and provide accurate reporting for stakeholders.
Expertise in Excel/Numbers for data analytics, including pivot tables and vlookup commands
Proficient in problem-solving and critical thinking, with a focus on innovation and continuous optimization (improving the quality of labels, reducing time to delivery and cost).
Project management experience conducting root cause analysis and performing deep dives into quality issues to drive process improvement.
Excellent written and verbal communication skills, possessing the ability to work efficiently with members of other data functions and maintaining clear and effective communication with stakeholders.
Self-starter, able to handle ambiguity, identify risks, troubleshoot, and find the right people and tools to get the job done.
Proven experience mentoring and/or growing team members, and establishing a robust collaborative culture.
Description
As part of the SIML Data QA team, you'll play a central role in enhancing Apple's customer experience by reviewing and verifying that all datasets supplied to R&D are complete, accurate, and consistent. We are committed to Data excellence, ensuring diversity, relevance, and integrity in our datasets to enable ML engineers to build AI solutions that are transformative, ethical, and impactful. Each year, we power dozens of features and work closely with ML teams across the entire company. In this position, you'll be accountable for setting up workflows and examining assets and labels of incoming datasets, ensuring that any data delivered to R&D meets Apple's rigorous quality standards. You'll work in a fast-paced, dynamic, technology-focused environment leveraging generative AI technologies to help evaluate data in partnership with a QA project manager. You'll review the Annotation Analyst evaluations and become the subject matter expert in your domain (text, camera, or Photoshop). You'll lead training for the annotators and provide quality feedback to the QA project managers overseeing the project. You'll use your analytical skills to track and report trends. You'll collaborate with team members and share ideas for business improvements. At Apple, our individual backgrounds, perspectives, and passions help us build the ideas that move all of us forward. We'll train you to be an expert in understanding, supporting, and improving the data quality experience.
Education & Experience
Bachelors degree in Business, Statistics, Computer Science, or equivalent practical experience.
Additional Requirements
Pay & Benefits
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