Data Scientist

Join Apple's HID Quality Engineering team to ensure our products exceed our customers' expectations! You'll work with QE and Algorithm teams to build metrics around algorithm performance, turning user behavior into quality specifications and measurable standards that teams can consistently apply. You will make sure new customer facing algorithms are validated effectively using data and repeatable processes. This includes defining the right data, ensuring quality of data and labeling, and running tests on datasets.

Description

This role is focused on defining algorithm quality. Day-to-day work involves writing quality specifications, establishing benchmarks, developing test scenario frameworks, and partnering closely with algorithm, platform, and UX research teams to identify where quality standards are missing or misaligned with user outcomes.

Minimum Qualifications

BS in EE, ECE, CS, Statistics, HCI, Cognitive Science, or a related field
3+ years of experience in quality engineering, test strategy, or algorithm/ML evaluation
Experience writing quality specifications or test plans for complex technical systems adopted by multiple teams
Experience with signal-level sensor algorithms
Familiarity with statistical methods used in algorithm evaluation, such as A/B testing, regression analysis, and significance testing
Working proficiency with Python for data exploration and analysis

Preferred Qualifications

MS or PhD in EE, ECE, CS, Statistics, HCI, Cognitive Science, or a related field
Strong understanding of ML and sensing system behavior, with the ability to reason about failure modes, edge cases, and the difference between a metric shifting and quality actually changing
Experience defining test scenario coverage models and setting benchmarks for systems where ground truth is ambiguous or user-dependent
Experience building consensus on quality standards across teams with competing priorities
Ability to write specifications precise enough for engineers to implement automation directly, without ambiguity
Background in UX research, HCI, or human factors, with experience grounding technical quality definitions in human behavior
Familiarity with embedded platform constraints
Experience with causal inference or advanced experimental design for algorithm evaluation