JobgetherRemote · Canada
1h ago
AmazonPosted 4w ago
L4
Data Scientist , Worldwide Global Selling -AIT
Data Scientist , Worldwide Global Selling -AIT at Amazon scores 91 out of 100 on AI centrality, which makes it a Level 4 role on this board.
CN, 31, Shanghaimidfull-time
AI in this role
ai-evaluation
The Worldwide Global Selling Analytics, Intelligence, and Technology (WWGS-AIT) team serves as the research, automation, and insight arm of the International Seller Service data hub, enabling rapid delivery of growth insights through strategic investments in regional data foundations, self-service business intelligence solutions, and artificial intelligence tools.
The WWGS-AIT team is positioned to establish AI-ready foundational capabilities across the WWGS organization while maintaining excellence in business insight generation, and self-service BI/AI application development.
WWGS-AIT is looking for a Data Scientist to build reusable science capabilities that support seller growth, operational decision-making, and cross-domain innovation across Worldwide Global Selling.
You will lead high-impact modeling initiatives at the intersection of graph science, machine learning, simulation, and optimization. Your initial focus will include building identity-resolution and entity-linkage capabilities that create a trusted One ID view across fragmented seller and business data; developing simulation and decision models for logistics and inventory options; and establishing reusable modeling foundations for seller lifecycle and other cross-domain use cases.
You will also partner with business, product, engineering, and analytics teams to evaluate and deliver prioritized science opportunities through a common intake process. This role is ideal for a hands-on scientist who can move from ambiguous business problems to robust, production-ready models and decision systems.
Key job responsibilities
Lead the design, development, and productionization of graph-based identity-resolution and entity-linkage models that connect seller, account, business, logistics, and other relevant entities into a trusted One ID foundation.
Develop simulation, optimization, forecasting, and decision-support models for logistics, inventory, and related operational choices; quantify trade-offs, uncertainty, and expected business impact.
Establish scalable model-development practices, including feature engineering, experiment design, model validation, monitoring, reproducibility, documentation, and responsible-use controls.
Translate ambiguous business questions into clear science problems, measurable hypotheses, model requirements, and decision frameworks.
Partner with Data Engineering, BIE, Product, and domain teams to build reliable data pipelines, model features, evaluation datasets, and production model interfaces.
Support prioritized science needs from Supply Chain, Seller Success, and other teams through the WWGS-AIT operating-planning intake and prioritization process.
Define model performance, business-impact, and operational-success metrics; use offline evaluation, back-testing, simulation, and controlled experiments to continuously improve solutions.
Contribute applied AI and GenAI expertise where it improves science-enabled products—for example, model evaluation, retrieval/ranking, intelligent decision support, or AI-agent capabilities grounded in trusted data and models.
Influence the WWGS science roadmap by identifying opportunities to convert repeated business problems into durable, reusable data and modeling capabilities.
Basic qualifications
- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- 1+ years of guiding and coaching a group of researchers experience
- 1+ years of working with or evaluating AI systems experience
- 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience
- Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
- Experience applying theoretical models in an applied environment
Preferred qualifications
- Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
- Knowledge of machine learning concepts and their application to reasoning and problem-solving
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company
- Experience in defining and creating benchmarks for assessing GenAI model performance
- Experience working on multi-team, cross-disciplinary projects
- Experience applying quantitative analysis to solve business problems and making data-driven business decisions
- Experience effectively communicating complex concepts through written and verbal communication
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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AI Evaluation
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- How do you decide that one model's output is better than another's for a given task?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
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