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The Data Science and Visualization (DataViz) team in Hardware Engineering at Apple is seeking an enthusiastic team player to join us as an Applied Data Scientist and Visualization Specialist. We are a small, supportive, inclusive, and dynamic team that works on a variety of problems in engineering and research, spanning analysis, visualization, 3D geometry, and more. The common thread through all of our work is transforming complex data into something understandable and actionable.
Our unique position within the organization allows us to collaborate with teams across a wide range of Apple products. The work we do impacts products used by millions of people worldwide.
Description
As a member of this team-oriented, collaborative group, you will be working on the latest, most innovative Apple products together with many cross-functional teams.
This role requires strong collaboration, fluency in using statistics and visualization to answer specific questions about datasets, and a passion for creating informative visualizations and presentations that communicate results to all levels of the company. We encourage anyone who approaches problem-solving with curiosity and imagination to apply. If you are someone who loves learning, views challenging questions as opportunities for deeper understanding, and enjoys uncovering patterns in data and sharing those insights with others, we would love to hear from you!
You will work closely with DataViz team members and members of other partner teams, with many opportunities to practice and grow communication skills. You'll have a lot of latitude to define how you work; specific tools matter less than the lateral thinking they enable. This is a truly unique opportunity that exposes you to many different teams at Apple.
Minimum Qualifications
No single candidate will come in with all desired qualifications - above all else, we're looking for someone with critical thinking skills as well as an eagerness and ability to learn new skills and solve dynamic problems in a collaborative, supportive, and inclusive environment.
Experience writing code and/or creating tools to import, clean, analyze, and visualize data to enable data-driven decision making.
A solid grounding in statistics and the judgment to know which methods a problem calls for. The work spans a range of dataset sizes and complexities, often calling for thoughtful statistical techniques alongside machine learning when appropriate.
Comfort building and evaluating machine learning models, with a focus on producing meaningful, interpretable results.
Strong interest in peer collaboration and teamwork and a willingness to solicit input and accept feedback.
An appreciation of functional design or an interest in developing your design aesthetic.
Eagerness to communicate important information in an easily understandable manner.
Enthusiasm for learning new tools and processes, and for helping collaborators grow their technical skills.
Master's degree (or equivalent professional experience) in an analytical field such as, but not limited to, Computer Science, Engineering, Statistics, Machine Learning, Applied Math, Physics, Biomechanics, Biological Sciences, Robotics, Climate/Environmental Science, Computer Graphics, or Animation
Preferred Qualifications
The varied nature of the problems our team handles means that there is a lot of room for your other skills to also be of use. We mention these here so you will include them in your resume if present, but they are not required for the role.
Prior experience working in engineering, manufacturing, biomechanics, and/or human factors-related fields.
Experience analyzing and visualizing 3D data sets or enthusiasm for delving deeper into 3D data analysis and visualization. Our datasets include human anthropometric and biometric data, acoustic data, and inertial measurement unit data (accelerometers, gyroscopes, altimeters).
Experience or interest in any of the following: natural language processing, computer vision, computational geometry, time series analysis, signal processing, or anomaly detection.
Published work or contributions to open-source ML projects.
Familiarity with recent advances in generative AI, foundation models, or transfer learning.
Ph.D. preferred.