Data Scientist

Apple

Apple's Video Engineering Data Analytics and Quality (DAQ) group is dedicated to addressing the challenges posed by evaluating machine learning algorithms. The team collaborates closely with domain experts and project leaders to enhance methodologies as new insights emerge. This role is pivotal in assessing risks, identifying gaps, and achieving significant results.

In this role you can expect to have the responsibilities:

• Analyze and validate machine learning and computer vision models against performance standards.
• Design and implement metrics for evaluating model effectiveness and accuracy.
• Conduct detailed failure analysis to identify improvement areas in models.
• Preprocess and clean large datasets for modeling purposes.
• Optimize models for performance and scalability using advanced techniques.
• Collaborate with cross-functional teams to integrate models into production.

This role comes with the following benefits:

• Opportunity to become an Apple shareholder through stock programs.
• Comprehensive medical and dental coverage.
• Retirement benefits.
• Discounted products and services.
• Tuition reimbursement for career-advancing formal education.
• Eligibility for discretionary bonuses or commission payments.

This role requires you to have:

• Proficiency in data science, machine learning, computer vision, and statistical analysis.
• Advanced programming skills in SQL and Python for data manipulation and processing.
• Expertise in analyzing machine learning model failures.
• Experience in conducting experiments and investigations.
• Skilled in data wrangling and data visualization, using tools like Tableau and AWS.

You would benefit from having:

• Experience in managing complex programs.
• Familiarity with machine learning interpretability methods.
• Knowledge of various foundation models like CGPT4V, LLAMA, and CLIP.
• Detail-oriented and capable of understanding complex algorithms.

Apple is committed to diversity and inclusion, providing equal employment opportunities to all applicants regardless of race, color, religion, gender, sexual orientation, or disability.