Crowdsourced Sensing Data Scientist

The Sensing and Connectivity team is committed to developing innovative technology that enhances user safety and makes our devices more intelligent. We are exploring how the incredible sensing, processing, and connectivity of iPhone can be leveraged to detect nearby hazards and deliver timely alerts. We are seeking a data scientist with a strong foundation in statistics, machine learning, and real-time analytics to help design and validate algorithms that operate reliably at a global scale.

Description

In this role, you will develop and evaluate statistical and ML-based algorithms using large-scale, crowdsourced sensor data from heterogeneous mobile and edge devices. You will build simulation and replay pipelines, refine data collection strategies, analyze real-time telemetry, and identify the features and signals that most influence model performance.

Minimum Qualifications

Graduate degree in Computer Science, Data Science, or equivalent experience. 5 years of experience in statistical inference and machine learning, PyTorch, TensorFlow 5 years of experience in feature evaluation, model robustness, and algorithm benchmarking 3 years of experience handling noisy, heterogeneous, crowdsourced sensor data 3 years of experience deploying on-device or resource-constrained models

Preferred Qualifications

Experience with real-time data systems (Kafka, Spark, Flink) Experience with sensor networks, IoT systems, or mobile sensing Experience with inertial sensing, time series analysis, signal processing, system identification Experience with multi-sensor fusion (accelerometer, gyroscope, barometer, GNSS)