AMD is hiring a data scientist and performance engineer to own the quantitative forecasting system for competitive server CPU performance.

Design and maintain a forecasting framework that connects CPU architecture, memory, I/O, power, operating-system and runtime behavior, and workload kernels.
Generate forward-looking forecasts for performance, performance-per-watt, and price-performance using confidence ranges instead of single-point estimates.
Build scenarios for uncertain competitor attributes such as frequency, core count, memory bandwidth, software maturity, launch timing, and platform power.
Track prediction error by workload, competitor, forecast horizon, and model version.
Decompose forecast errors and tune models as measured systems become available.
Develop normalized competitive scorecards spanning workload performance, efficiency and TCO, memory and I/O, software ecosystem, deployability, and roadmap credibility.
Fuse benchmark results, performance counters, partner telemetry, public roadmaps, software changes, and platform evidence while preserving source provenance.
Identify leading indicators that materially change the forecast and surface early risk or opportunity signals.
Partner with architecture and workload leads to run design experiments that reduce the highest-value uncertainties.
Create executive-grade visualizations for deltas, confidence ranges, scenarios, drivers, and forecast-versus-actual history.
Write quarterly forecast narratives and support rapid recalibration when new silicon or material evidence appears.

Demonstrated experience building quantitative models used for technical or business decisions under uncertainty.
Strong programming and data-analysis skills in Python or an equivalent analytical environment.
Expertise in defining meaningful error metrics, calibration methods, and sensitivity analyses for sparse or biased data.
Working knowledge of server CPU and system performance, plus willingness to engage deeply with architectural causality.
Experience building reproducible data pipelines, using versioning, and working with notebooks or scripts while maintaining source provenance.
Strong visualization, writing, and presentation skills for technical and executive audiences.
Sound judgment on when a model is useful, when it is overfit, and when evidence does not support a precise conclusion.
Experience with CPU or GPU performance prediction, pre-silicon modeling, capacity planning, forecasting, or benchmark analytics.
Experience with Bayesian modeling, Monte Carlo simulation, probabilistic programming, or uncertainty quantification.
Experience combining structured benchmark data with semi-structured roadmap, software-change, or market evidence.
Cross-ISA or cloud-instance price-performance analysis experience.
Familiarity with platform economics, TCO, rack power, or density.

The Competitive Advanced Performance (CAP) team is a new, visible capability focused on predicting, validating, and explaining competitive server CPU performance before products reach the market.
Joining now means helping define the methods, tools, and operating model from the ground up.

Own the quantitative forecasting system behind the CAP team, combining architecture assumptions, workload measurements, software trends, platform data, and partner validation.
Produce forward-looking predictions with explicit confidence ranges, accountable forecast-versus-actual learning, and a competitive narrative covering likely leadership, risk areas, confidence, and variables that could change the outcome.

Bachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field

Principal Competitive CPU Performance Forecaster & Data Scientist

Job Description