Data Scientist - Pricing & Profitability

Advanced Micro Devices, Inc•San Jose, CA

3d•Hybrid

About The Position

AMD's AECG Pricing & Profitability Optimization team turns data into pricing decisions that protect and grow gross margin across our adaptive and embedded portfolio. We are hiring a Data Scientist to build and advance how we apply AI to pricing — building the AI/LLM tooling, machine-learning models, analytics platforms, and automation that give the wider organization leverage on pricing decisions. This is a hands-on, high-impact role for someone who is equally comfortable building agentic AI workflows, developing predictive models, writing production Python against Snowflake, and translating numbers into a story the business can act on. You are a hands-on data scientist who enjoys solving complex business problems through a combination of AI, machine learning, analytics, and automation. You are comfortable building AI-powered solutions from concept to production, working with large-scale data platforms, and translating technical insights into business impact. In this role, you will help build and advance how our team applies AI to pricing by designing, building, and maintaining the AI tooling and data platforms that give the wider organization leverage, alongside the analytics that inform pricing and margin decisions.

Requirements

Hands-on data science / analytics experience, ideally supporting pricing, finance, sales operations, or commercial analytics function.
Hands-on experience with LLMs / generative AI — building with LLM APIs, prompt engineering, retrieval-augmented generation (RAG), or agent frameworks — and a genuine enthusiasm for applying AI to real business problems.
Strong Python for data work — pandas, data pipelines, automation, and calling APIs — ideally with a track record of writing production-quality, maintainable code.
Proficient in SQL and comfortable working with a cloud data platform; experience with Snowflake
Applied Machine Learning — hands-on experience building, testing, and deploying ML models for pricing, forecasting or predictive analytics, applied to business or financial problems.
Ability to translate complex models into explainable, auditable insights suitable for executive audiences — strong storytelling that influences business decisions.
A results-oriented bias for action, thrives on solving complex problems in a fast-paced environment and can scope, execute, and deliver initiatives.
Solid business acumen and the ability to connect analysis to gross-margin and pricing outcomes; comfortable engaging directly with stakeholders.
Preferred exposure to pricing systems and familiarity with CPQ, quote-to-cash, or Deal Desk processes.
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Business Analytics, Engineering, Finance, or a related quantitative field.

Responsibilities

Build and own AI-driven pricing tools. Design, develop, document, and continuously improve internal AI/LLM-based tooling that automates pricing analysis and business workflows owning both the architecture and the day-to-day operation.
Drive new AI use cases. Prototype and evaluate emerging AI approaches - agentic workflows, retrieval-augmented generation, and reusable AI skills to turn manual, repetitive pricing processes into automated workflows that deliver measurable productivity gains.
Build predictive models. Develop, test, and refine machine learning models for pricing and margin forecasting, and apply statistical and ML techniques to large, complex datasets.
Engineer reliable data pipelines. Query and model structured data in Snowflake and other sources; write clean, reproducible, well-documented code that others on the team can pick up and run.
Automate reporting pipelines. Build tooling that automatically generates recurring reporting, commentary, alerts, and decision-support from underlying data, scripts, and models, replacing manual assembly of KPI and QBR executive outputs with reliable, repeatable automation.
Operationalize and monitor what you build. Deploy models and tools into production and own their ongoing monitoring, recalibration, and performance tracking so they stay accurate and trusted over time.
Own a data-analytics platform. Take ownership of a cross-functional platform that consolidates quote, cost, and point-of-sale data into a single source of truth for pricing insight — maintaining the data pipeline, refresh process, and the analytical views leaders rely on.
Partner across the business. Work with Pricing, BizOps, Sales, and Business Management Systems to understand requirements, validate outputs, and make sure the models, analytics, and AI tools solve real business problems.