Power/Performance Data Scientist, Platform Architecture
Santa Clara Valley (Cupertino), California, United States
We’re looking for a driven, ambitious engineer who loves to test their skills against new and diverse challenges. We have a role that will push you to do your best, to be your most creative, and to invent new technology that affects billions of users. If so, become part of an elite team responsible for the hardware and controls architecture of all Apple platforms.
- Excellent mathematical expertise in statistical methods, signal processing and machine learning concepts.
- High programming proficiency (C/C++, Python, and Matlab or equivalent)
- Innovative and critical thinker with proven analytical skills and innate curiosity
- Highly professional, collaborative, with extraordinary communication and presentation skills
- Deep understanding of probability, statistics and techniques for dataset visualization
- General understanding of hardware and software architecture of mobile systems.
You will apply a variety of formal data analytics methods to explore and improve device power and performance. You will bring to light system behavior by examining measured data. Beyond mere numerical analysis, you will find actionable insights and recommend improvements to control algorithms and architecture. We are looking for an engineer with strong mathematical background on signal processing, statistical methods and machine learning algorithms. Solid understanding of control systems, as well as hardware and software architecture of mobile systems is critical. Responsibilities: Apply data analytics and machine learning to measured device perf/power data sets, and perform multi-variable sensitivity analysis. Explore impact of system control algorithms on power/performance. Use data-driven analysis to quantify and explain multi-functional trade-offs between system design, power consumption and user experience to an especially sophisticated multi-disciplinary team of engineers. Propose, develop and evaluate system management policies based on gained insights. Share methods and results with hardware, software and test teams.
Education & Experience
PhD or MS in Electrical/Computer Engineering, Mathematics/Statistics or Computer Science plus minimum of 2 years relevant experience.