Sr. Data Analytics Manager

Advanced Micro Devices, Inc•Santa Clara, CA

About The Position

AMD is seeking a Senior Manager of Marketing Data Science to build and scale data science capabilities within the Marketing Analytics team. This person will identify the highest-impact analytics opportunities across the full marketing funnel, translate them into actionable projects, and lead the work to deliver them. Part of this role will include building the data science capability from the ground up, developing and deepening relationships with stakeholders across Marketing, Product, and Sales Operations, and collaborating across teams to educate and influence the broader marketing organization on the effective use of data science. This person will own the marketing data science agenda, ensuring that data-driven insights are embedded in decision-making, campaigns and investments are continuously optimized, and marketing impact is more effectively measured and improved.

Requirements

10+ years of experience in data science, advanced analytics, marketing analytics, or a related discipline, including experience leading complex, cross-functional analytics initiatives.
Demonstrated success building or scaling a data science or advanced analytics capability, including defining the roadmap, operating model, methodologies, technology requirements, and governance.
Hands-on knowledge of advanced analytical approaches such as marketing mix modeling, multi-touch attribution, experimentation, forecasting, segmentation, propensity modeling, causal inference, and campaign optimization.
Strong understanding of marketing and sales data ecosystems, including CRM, marketing automation, digital analytics, paid media, account-based marketing, customer, opportunity, and product data.
Proficiency with analytical tools and technologies such as Python or R, SQL, cloud data platforms, machine learning frameworks, visualization tools, and modern data science development environments.
Experience partnering with data engineering, IT, and platform teams to improve data quality, integrate complex data sources, operationalize models, and develop scalable analytical solutions.
Proven ability to build trusted relationships with senior stakeholders across Marketing, Product, Sales Operations, Finance, and Technology, and to influence decisions without direct authority.
Exceptional communication and storytelling skills, with the ability to translate complex analytical findings into clear business implications and recommendations for technical, marketing, and executive audiences.
Experience developing and mentoring data scientists or analytics professionals and fostering a culture of experimentation, continuous learning, and measurable business impact.
Bachelor’s degree in Data Science, Statistics, Mathematics, Economics, Computer Science, Engineering, Marketing Analytics, or a related quantitative field.

Nice To Haves

A master’s degree or equivalent advanced experience is preferred.

Responsibilities

Build and scale Marketing Data Science capabilities from the ground up, including the operating model, analytical frameworks, tools, processes, and governance required to support the broader marketing organization.
Develop and own the Marketing Data Science roadmap, identifying and prioritizing high-impact analytics opportunities across the complete marketing funnel—from awareness and engagement through demand generation, pipeline, conversion, retention, and expansion.
Measure and demonstrate the business impact of Marketing Data Science, establishing performance indicators and feedback loops that continuously improve models, campaigns, customer experiences, and marketing investments.
Design and deliver advanced analytical solutions, including lead and account scoring, attribution, marketing mix modeling, experimentation, forecasting, segmentation, propensity modeling, and campaign optimization.
Partner closely with Marketing, Product, Sales Operations, and other cross-functional stakeholders to understand business priorities, identify opportunities, and embed analytics into planning and decision-making processes.
Ensure analytical solutions are scalable, reliable, and actionable, partnering with data engineering, technology, and platform teams to improve data quality, accessibility, integration, and model deployment.