In this position, the candidate will be working for the Hardware Validation Engineering (HVE) team on post-Silicon hardware system validation of next generation Mac systems. Our Engineering Team is responsible for architecting methods to test new system hardware with the macOS environment to catch issues early in the hardware life cycle. You will be a key technical and logistical contributor and will utilize your in-depth data analytic skills to help provide insight into Hardware behaviors related to Silicon and System performance, power, temperature and fault/error issues.\\n
As a Data Scientist / Analyst within the Hardware team your job responsibilities will include:\n\nAnalyze large-scale hardware telemetry including power rails, thermal sensors, Power Silicon and SoC counters and performance logs, and stress-test measurements etc..\nApply statistical modeling and ML techniques to identify anomalies, drift, hardware degradation patterns, and emerging failure signatures.\nDevelop feature extraction pipelines tailored to silicon behavior-examples: rate-of-change of thermal zones, correlation of voltage droops with workload transitions, PMU-based bottleneck signatures.\nBuild predictive models that estimate performance/power deviations, reliability risks, or stress-induced failures.\nCreate clear, high-signal visualizations (e.g., multi-axis time-series overlays, workload-power envelopes, thermal gradients, event timelines) to support hardware debug and performance analysis.\nAutomate root-cause discovery workflows using statistical correlations, temporal pattern detection, clustering of abnormal runs, and hardware-aware signal decomposition.\nWork closely with silicon design, system validation, and performance engineering teams to turn data insights into actionable design or validation recommendations.\nContinuously refine modeling and feature engineering methods as new hardware blocks, sensors, counters, and test modes become available.\nData Extraction, Parsing and Storage own the pipeline for extraction and storage into Databases and the structure.
Bachelor's degree in Computer Engineering, or Computer Science, with 8+ years of experience working with Data Analysis.\n\nData Science / Statistical Expertise: Strong foundation in Multivariate statistical analysis, Hypothesis testing and experimental design, Time-series modeling (ARIMA, LSTM/GRU ), Anomaly detection (Isolation Forest, HDBSCAN, PCA/ICA, etc). Ability to handle asynchronous, high-frequency hardware telemetry and remove jitter, outliers, and measurement noise.\n\nProgramming & Tools: Expert Python skills (Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, Plotly). Ability to write clean, reproducible analytical scripts for large datasets. Strong SQL and experience working with instrumentation data, structured log stores, or validation data warehouses.\n\nVisualization Skills: Ability to produce high-quality, engineering-oriented data visualizations that reveal trends, anomalies, and causal relationships. Must be comfortable creating custom plots and interactive exploratory visualizations using Python and other analytical tools.\n
Experience with dashboard platforms (Grafana, InfluxDB, Plotly Dash, Tableau).\nFamiliarity with test automation data, debug logs, or hardware lab measurement tools.\nExperience in product validation, reliability, performance characterization and/or silicon bring-up.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 90733111
- Position Id: f34be3c164e4a630c10053d1b46f2d87
- Posted 12 hours ago
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