亚马逊Sr. AI Product Manager, Seller Education
任职要求
基本任职资格
- 7+ years of product or program management, product marketing, business development or technology experience
- Bachelor's degree or equivalent
- Proven experience owning and driving product roadmap strategy, definition, and execution for technology products.
- End‑to‑end ownership of product delivery, from discovery and requirements through launch and iteration.
- Experience with Machine Learning and Large Language Model (LLM) fundamentals, and working with AI/ML teams.
- Demonstrated ability to communicate complex technical topics to both technical and non‑technical audiences, including executive stakeholders.
优先任职资格
- Experience influencing and driving decisions with senior leadership in large, complex organizations.
- Experience develop…工作职责
Product Strategy & Development * Own the product vision, strategy, and roadmap for AI-powered Seller Education experiences. * Identify and prioritize learning opportunities across the seller lifecycle (e.g., onboarding, growth, compliance, optimization). * Design and launch AI solutions (e.g., adaptive learning, content recommendation, conversational tutors) that improve learning outcomes and engagement. * Drive measurable improvements in Seller learning efficiency, satisfaction, and business impact. * Lead continuous product improvement using experiments, data insights, and qualitative feedback from Sellers and stakeholders. Technical Leadership * Partner with data science and engineering teams to design, build, and scale AI/ML and LLM-based learning features. * Define clear technical requirements, success metrics, and guardrails for AI-powered education workflows. * Oversee end‑to‑end product development cycles, from discovery and design through implementation and launch. * Promote data-informed decision making and experimentation across all product initiatives. Stakeholder & Program Management * Lead cross‑functional collaboration between product, program, tech, content, marketing, and operations teams. * Communicate product strategy, roadmap, and results to senior leadership, highlighting trade‑offs and impact. * Drive alignment and change management across CN and global teams to scale adoption of AI‑powered learning solutions. * Influence internal and external partners to co‑create high‑quality, localized, and relevant learning experiences for Sellers.
Product Strategy & Development - Own the product vision, strategy, and roadmap for AI products in compliance domains - Identify and prioritize high‑impact opportunities - Design and launch AI-powered solutions that reduce friction and improve Seller experience - Drive measurable improvements in efficiency, automation, and decision quality - Lead continuous product optimization using experimentation, data insights, and user feedback. Technical Leadership - Partner with data science and engineering teams to design, build, and scale AI/ML and LLM-based solutions. - Define clear technical requirements, success metrics, and guardrails for AI-powered features and workflows. - Oversee end‑to‑end execution cycles, ensuring on‑time delivery, reliability, and strong business outcomes. - Champion data‑informed decision making across product initiatives, leveraging experimentation and A/B testing where appropriate. Stakeholder Management - Lead cross‑functional collaboration between central product/program teams, tech teams, operations, legal/compliance, and external partners. - Communicate product strategy, roadmap, and impact clearly to senior leadership, including trade‑offs and decision rationale. - Drive alignment and change management across CN and global teams, ensuring scalable adoption of AI solutions. - Influence stakeholders at multiple levels to remove roadblocks and accelerate delivery.
Technical Development & Architecture * Design and implement scalable AI/ML solutions for Compliance use cases * Lead the development of efficient ML models and end‑to‑end data processing pipelines from ingestion to serving. * Build robust, production-grade AI services using Python and modern ML frameworks. * Make and document sound architectural decisions, ensuring systems are scalable, secure, and cost‑effective. * Establish and maintain high engineering standards, including testing, monitoring, and documentation. Engineering Leadership * Partner closely with data scientists, product managers, and operations teams to deliver end‑to‑end AI/ML solutions. * Define and evolve the technical architecture for AI-powered features and platforms. * Lead code reviews, enforce best practices, and elevate engineering quality across the team. * Continuously improve AI system performance, reliability, and latency through experimentation and optimization. Technical Collaboration & Operations * Work with cross‑functional partners to understand requirements, refine scope, and prioritize technical work. * Provide technical guidance and mentorship to junior and mid‑level engineers. * Collaborate with platform and DevOps teams to ensure smooth deployment, monitoring, and maintenance of AI systems. * Implement and evolve ML Ops practices (e.g., CI/CD for models, feature stores, model monitoring, and retraining workflows).
• Define and deliver large-scale BI solutions, including data modeling, KPI standardization, and pipeline automation. • Lead migration of legacy reporting into modern AWS QuickSight and other BI platforms. • Establish and maintain data foundations for social and marketing data, ensuring completeness, accuracy, and compliance. • Conduct advanced analytics, including user behavior modeling, segmentation, and marketing campaign measurement. • Generate actionable insights and recommendations to improve seller journeys, marketing effectiveness, and AI agent performance. • Collaborate with marketing operation team, data engineers, product managers, SDEs, financial analysts, and data scientists to design metrics and guide business decisions. • Drive best practices in operational excellence, data quality management, and data governance. A day in the life You will define marketing BI strategy, build scalable data models and generate actionable insights that drive product, marketing, and AI initiatives. You will partner with data engineering, product, and science teams to ensure high-quality, compliant, and scalable data foundations. Your work will directly influence AI-powered agents, seller engagement, and business growth.
· Translating business questions and concerns into specific analytical questions that can be answered with available data using BI tools; produce the required data when it is not available. · Apply Statistical and Machine Learning methods to specific business problems and data. · Create global standard metrics across regions and perform benchmark analysis. · Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc. · Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions. · Collaborate with colleagues from multidisciplinary science, engineering and business backgrounds. · Develop efficient data querying and modeling infrastructure. · Manage your own process. Prioritize and execute on high impact projects, triage external requests, and ensure to deliver projects in time. · Utilizing code (SQL, Python, R, etc.) for analyzing data and building statistical models.