Demographic Data Scientist

Apple is currently seeking an enthusiastic team player to join a dynamic and collaborative group as a demographic data scientist focusing on human populations using statistics, computer programming, and machine learning within a human factors design team.

You’ll have the unique opportunity to contribute to the development and continuing improvement of Apple hardware products by distilling data into actionable and intuitive visualizations from human interaction perspective.

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 and curiosity in the application of different methods to ambiguous problems.

Success in this role means being fluent in using statistics and visualization tools to answer specific questions about datasets regardless of sample size, and a passion about communicating solutions to dynamic problems through presentations.

MS +3 years of experience or PhD in an analytical field such as (but not limited to) Quantitive Social Sciences (Sociology, Education, Economics, Political Science), Computer Science, Engineering, Statistics, Geographic Information Systems, Applied Math, Physics, Biological Sciences, Climate or Environmental Science.

Equipped with deep knowledge of statistical analysis and modeling tools, along with a strong foundation in linear algebra, geometry, optimization, or inference techniques. Strong programming skills in a scripting language (Python, R, Matlab, etc), using these tools to model and visualize data to enable data-driven decision making.

Possess excellent communication and interpersonal skills, attention to detail, patience, and aesthetic sensibility. Demonstrated experience working in teams and collaborating with peers, with a willingness to solicit input and accept feedback.

Experience making inferences from small-scale datasets Experience with population modeling, inference, weighting, and simulation techniques (e.g. Monte Carlo methods) to understand and estimate variation and uncertainty. Experience with statistical methods such as Bayesian methods to identify trends and anomalies in multivariate and/or medium-sized and/or large-scale datasets.

Experience with human anthropometric, biometric, or perception/preference data, and/or a willingness to delve deeper in to these data types. Experience using generative AI tools in workflows.