{"id":2910867,"url":"https://alion.io/job/amd-principal-competitive-cpu-performance-forecaster-data-scientist","title":"Principal Competitive CPU Performance Forecaster & Data Scientist","company":{"id":93,"name":"AMD","domain":"amd.com","url":"https://alion.io/company/amd","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jibe","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":135000,"max_usd":265000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":415},"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Pure Data","optional":false},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-09-18T18:07:00Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-21T21:15:51Z","board_verified":true,"closed_at":null,"days_open":33,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":33},"description":"ADVANCE YOUR CAREER. ADVANCE THE WORLD.\nAt AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.\nWhether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger - technology that moves the world forward. Join us and, together, we’ll advance your career.\nTHE TEAM:\nThe 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.\nTHE ROLE:\nAMD is seeking a data scientist and performance engineer to own the quantitative forecasting system behind the CAP team. You will combine architecture assumptions, workload measurements, software trends, platform data and partner validation into forward-looking predictions with explicit confidence ranges. The role is accountable for model error, scenario analysis, forecast-versus-actual learning and a clear competitive narrative: where AMD is likely to lead, where it may face risk, how confident the team is and which variables could change the outcome.\nTHE PERSON:\nYou are neither a pure data scientist who treats performance data as opaque features nor a benchmark engineer who reports only point results. You understand enough CPU and system performance to model causality, and enough statistics to avoid false precision. You care about provenance, versioning and calibration, and you can make uncertainty understandable to engineers and executives.\nKEY RESPONSIBILITIES:\nDesign and maintain a forecasting framework that links CPU architecture, memory, I/O, power, operating-system and runtime behavior, and workload kernels.\nCreate performance, performance-per-watt and price-performance forecasts with confidence ranges rather than single-point estimates.\nBuild scenarios for uncertain competitor attributes such as frequency, core count, memory bandwidth, software maturity, launch timing and platform power.\nTrack prediction error by workload, competitor, forecast horizon and model version; decompose errors and tune the model as measured systems become available.\nDevelop normalized competitive scorecards across workload performance, efficiency and TCO, memory and I/O, software ecosystem, deployability and roadmap credibility.\nFuse benchmark results, performance counters, partner telemetry, public roadmaps, software changes and platform evidence while preserving source provenance.\nIdentify leading indicators that materially change the forecast and surface early risk or opportunity signals.\nPartner with architecture and workload leads to design experiments that reduce the most valuable uncertainties.\nBuild executive-grade visualizations of deltas, confidence ranges, scenarios, drivers and forecast-versus-actual history.\nWrite quarterly forecast narratives and support rapid recalibration when new silicon or material evidence appears.\nPREFERRED EXPERIENCE:\nDemonstrated experience building quantitative models used for technical or business decisions under uncertainty.\nStrong programming and data-analysis skills in Python or an equivalent analytical environment.\nExpertise in defining meaningful error metrics, calibration methods and sensitivity analyses for sparse or biased data.\nWorking knowledge of server CPU and system performance and a willingness to engage deeply with architectural causality.\nExperience with reproducible data pipelines, versioning, notebooks or scripts, and source provenance.\nStrong visualization, writing and presentation skills for technical and executive audiences.\nSound judgment about when a model is useful, when it is overfit and when the available evidence does not support a precise conclusion.\nCPU or GPU performance prediction, pre-silicon modeling, capacity planning, forecasting or benchmark analytics.\nBayesian modeling, Monte Carlo simulation, probabilistic programming or uncertainty quantification.\nExperience combining structured benchmark data with semi-structured roadmap, software-change or market evidence.\nCross-ISA or cloud-instance price-performance analysis.\nFamiliarity with platform economics, TCO, rack power or density.\nWHY THIS OPPORTUNITY STANDS OUT:\nBuild the quantitative engine at the center of a new technical organization.\nMake uncertainty actionable instead of hiding it behind a single number.\nWork with architects and workload experts who can test model assumptions against real systems.\nCreate a visible body of work that connects data science directly to product and architecture decisions.\nACADEMIC CREDENTIALS:\nBachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field\nLOCATION: Austin, Texas\nThis role is not eligible for visa sponsorship.\nBenefits offered are described: AMD benefits at a glance.\nAMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.\nAMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.\n\nThis posting is for an existing vacancy.\nTHE TEAM:\nThe 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.\nTHE ROLE:\nAMD is seeking a data scientist and performance engineer to own the quantitative forecasting system behind the CAP team. You will combine architecture assumptions, workload measurements, software trends, platform data and partner validation into forward-looking predictions with explicit confidence ranges. The role is accountable for model error, scenario analysis, forecast-versus-actual learning and a clear competitive narrative: where AMD is likely to lead, where it may face risk, how confident the team is and which variables could change the outcome.\nTHE PERSON:\nYou are neither a pure data scientist who treats performance data as opaque features nor a benchmark engineer who reports only point results. You understand enough CPU and system performance to model causality, and enough statistics to avoid false precision. You care about provenance, versioning and calibration, and you can make uncertainty understandable to engineers and executives.\nKEY RESPONSIBILITIES:\nDesign and maintain a forecasting framework that links CPU architecture, memory, I/O, power, operating-system and runtime behavior, and workload kernels.\nCreate performance, performance-per-watt and price-performance forecasts with confidence ranges rather than single-point estimates.\nBuild scenarios for uncertain competitor attributes such as frequency, core count, memory bandwidth, software maturity, launch timing and platform power.\nTrack prediction error by workload, competitor, forecast horizon and model version; decompose errors and tune the model as measured systems become available.\nDevelop normalized competitive scorecards across workload performance, efficiency and TCO, memory and I/O, software ecosystem, deployability and roadmap credibility.\nFuse benchmark results, performance counters, partner telemetry, public roadmaps, software changes and platform evidence while preserving source provenance.\nIdentify leading indicators that materially change the forecast and surface early risk or opportunity signals.\nPartner with architecture and workload leads to design experiments that reduce the most valuable uncertainties.\nBuild executive-grade visualizations of deltas, confidence ranges, scenarios, drivers and forecast-versus-actual history.\nWrite quarterly forecast narratives and support rapid recalibration when new silicon or material evidence appears.\nPREFERRED EXPERIENCE:\nDemonstrated experience building quantitative models used for technical or business decisions under uncertainty.\nStrong programming and data-analysis skills in Python or an equivalent analytical environment.\nExpertise in defining meaningful error metrics, calibration methods and sensitivity analyses for sparse or biased data.\nWorking knowledge of server CPU and system performance and a willingness to engage deeply with architectural causality.\nExperience with reproducible data pipelines, versioning, notebooks or scripts, and source provenance.\nStrong visualization, writing and presentation skills for technical and executive audiences.\nSound judgment about when a model is useful, when it is overfit and when the available evidence does not support a precise conclusion.\nCPU or GPU performance prediction, pre-silicon modeling, capacity planning, forecasting or benchmark analytics.\nBayesian modeling, Monte Carlo simulation, probabilistic programming or uncertainty quantification.\nExperience combining structured benchmark data with semi-structured roadmap, software-change or market evidence.\nCross-ISA or cloud-instance price-performance analysis.\nFamiliarity with platform economics, TCO, rack power or density.\nWHY THIS OPPORTUNITY STANDS OUT:\nBuild the quantitative engine at the center of a new technical organization.\nMake uncertainty actionable instead of hiding it behind a single number.\nWork with architects and workload experts who can test model assumptions against real systems.\nCreate a visible body of work that connects data science directly to product and architecture decisions.\nACADEMIC CREDENTIALS:\nBachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field\nLOCATION: Austin, Texas\nThis role is not eligible for visa sponsorship.\nBenefits offered are described: AMD benefits at a glance.\nAMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.\nAMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.\n\nThis posting is for an existing vacancy.","description_format":"text","description_chars":11470,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","Computer Components","AI Chips & Accelerators"],"lifecycle":[{"event":"open","at":"2026-10-20T09:28:01Z"}],"visa":[],"liveness":{"score":23,"band":"cold","label":"Long shot","p_open":1,"p_active":0.657,"p_room":0.35,"age_days":32,"expected_fill_days":15,"reasons":["conf:3","velocity","win:tail","comp:brand"],"computed_at":"2026-10-21T05:46:15Z"},"pay":null,"html_url":"https://alion.io/job/amd-principal-competitive-cpu-performance-forecaster-data-scientist","json_url":"https://alion.io/job/amd-principal-competitive-cpu-performance-forecaster-data-scientist.json","meta":{"generated_at":"2026-10-22T00:17:59Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":389,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-23T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":93},"rest":"https://alion.io/mcp/rest/get_company?id=93"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Famd-principal-competitive-cpu-performance-forecaster-data-scientist"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Famd-principal-competitive-cpu-performance-forecaster-data-scientist"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Famd-principal-competitive-cpu-performance-forecaster-data-scientist"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/amd-principal-competitive-cpu-performance-forecaster-data-scientist\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Famd-principal-competitive-cpu-performance-forecaster-data-scientist"}]}