Camera Machine Learning - Data Scientist

Role Number : 200618430-0836

Summary

Apple delivers the most popular cameras in the world. Each product release provides breakthroughs in photography with stunning camera features that customers love. Our cameras deploy imaging complexity at the frontier of traditional camera engineering methods. Data volumes are growing to meet this need across camera simulations, performance calibrations, measurement results, and their correlations. Our need to incorporate perception and product decision-making into this data through analytics, pattern finding, prediction models, correlations, and rapid data visualizations is critical to continue delivering the most amazing experiences for our customers.

Description

As a Data Scientist, your work will build on our powerful data engineering platform and custom team tools by applying data science, meaningful insights, and related data innovations directly to our shipping camera products and teams.

Minimum Qualifications

BS in physics, computer science, optics, image science, electrical engineering, data engineering, data science, or related fields and a minimum 10 years of relevant industry experience

Experience in data analysis and hardware performance metrics

Experience in data visualization techniques or dashboard creation

Experience coding in Python

Preferred Qualifications

MS or PhD in physics, computer science, optics, image science, electrical engineering, data engineering, data science, or related fields

Experience with ML development for image science applications, PCA, predictions, and trends

Experience developing and / or using large-scale deployed data pipelines

Experience in applied computer vision methods

Experience in cloud engineering tools such as AWS, Snowflake, or similar

Experience developing and / or using modern database technologies, including PostgreSQL, MongoDB

Experience in optical sensing concepts and understanding of related performance metrics

Experience with presenting data to Sr. management

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