Principal Competitive CPU Performance Forecaster & Data Scientist

Advanced Micro Devices, Inc•UNAVAILABLE, Texas

3d•Hybrid

About The Position

AMD is seeking a data scientist and performance engineer to own the quantitative forecasting system behind the Competitive Advanced Performance (CAP) team. This new, visible capability is focused on predicting, validating, and explaining competitive server CPU performance before products reach the market. The role involves combining architecture assumptions, workload measurements, software trends, platform data, and partner validation into forward-looking predictions with explicit confidence ranges. The individual will be accountable for model error, scenario analysis, forecast-versus-actual learning, and developing a clear competitive narrative, identifying areas where AMD is likely to lead, potential risks, confidence levels, and variables that could alter outcomes. Joining now offers the opportunity to help define the methods, tools, and operating model from the ground up.

Requirements

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 and a willingness to engage deeply with architectural causality.
Experience with reproducible data pipelines, versioning, notebooks or scripts, and source provenance.
Strong visualization, writing and presentation skills for technical and executive audiences.
Sound judgment about when a model is useful, when it is overfit and when the available evidence does not support a precise conclusion.
Bachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field.

Nice To Haves

CPU or GPU performance prediction, pre-silicon modeling, capacity planning, forecasting or benchmark analytics.
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.
Familiarity with platform economics, TCO, rack power or density.

Responsibilities

Design and maintain a forecasting framework that links CPU architecture, memory, I/O, power, operating-system and runtime behavior, and workload kernels.
Create performance, performance-per-watt and price-performance forecasts with confidence ranges rather than 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 errors and tune the model as measured systems become available.
Develop normalized competitive scorecards across 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 design experiments that reduce the most valuable uncertainties.
Build executive-grade visualizations of 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.