Applied Scientist II, Global Trade
at Amazon
Posted 7 hours ago
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This Applied Scientist II role focuses on solving machine learning problems for Amazon's Seller and Fulfilment Tech (SFT) organization, spanning model development, experimentation, and ML lifecycle excellence. The role emphasizes advancing ML science, experimentation methodologies, and exploration of Generative AI and LLMs, with a strong focus on research and publications. Key areas include selection recommendations, registration improvements, bad actor detection and prevention, selection economics, inventory recommendations, delivery promise predictions, and seller success, requiring deep problem formulation and cross-team collaboration. The position aims to apply and extend ML techniques to address business problems at project scale and contribute to internal or external publications.
The overarching goal of the team is to enhance ML expertise and fluency within SFT and across IST, championing engineering and operational excellence in ML model development and other related parts of the ML model lifecycle. Some of the key areas which the team owns in this space area:
Selection Recommendations, Registration improvements, Bad actor detection and prevention
Selection economics, Inventory recommendation, Delivery Promise Predictions, Seller success.
Within the ML space, the scientist would have to solve intrinsically hard problems where neither problem nor solution is well defined. So, the leader should have high focus on building a deep understanding of the ML science space, experimentation methodology, as well as a high focus on embracing external trends, especially applications of GenerativeAI and LLMs.
A large focus area for the role is to also contribute towards the science and research aspects. This role applies and extends existing scientific techniques, and invents new ones to address specific customers’ needs or business problems, at a project level. This should also lead to regular contributions to internal or external peer-reviewed publications that validate novelty
Basic Qualifications
- 3+ years of building models for business application experience- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Preferred Qualifications
- Experience using Unix/Linux- Experience in professional software development
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