Applied Scientist II, Cross Border Science and Analytics

The Cross-Border (XB) Science & Analytics team is at the heart of Amazons international marketplace expansion powering science-driven solutions that enable customers across 20 countries to discover and purchase products seamlessly across borders. Our work directly impacts millions in annualized business value through ML models algorithms and data-driven systems that solve some of Amazons most complex cross-border challenges.

We are a lean high-impact team of scientists working across various programs spanning search ranking demand forecasting pricing optimization product recommendations language understanding and generative AI. We partner closely with product engineering and business teams across Amazons global retail organization to take science from ideation to production at scale.

Key job responsibilities
We are looking for a passionate and technically strong Applied Scientist to join our this role you will design develop and deploy machine learning models and algorithms that directly improve the cross-border shopping experience for millions of Amazon customers worldwide. You will work on challenging ambiguous problemsfrom improving search relevance across languages to building ML-powered pricing and recommendation systemswith significant autonomy and end-to-end ownership.

This is a hands-on high-visibility role. You will publish your research internally and externally collaborate with world-class scientists and engineers and see your work go live across Amazons global marketplaces.

A day in the life
Research & Experimentation

Analyze large-scale datasets to identify patterns formulate hypotheses and design experiments

Develop and iterate on ML models (deep learning NLP ranking causal inference) to improve cross-border product discovery relevance and conversion

Design and run A/B experiments on live traffic to measure model impact against business and customer metrics

Building & Shipping

Write production-quality code (Python Java/Scala) and work with SDEs to deploy models into real-time and batch inference pipelines

Build end-to-end ML pipelinesfrom data ingestion and feature engineering to training evaluation and online serving

Own model monitoring performance debugging and iterative improvements post-launch

Collaboration & Communication

Participate in weekly science syncs design reviews and cross-functional standups with product managers engineers and business stakeholders

Translate business problems into well-defined science problems and communicate results and trade-offs to both technical and non-technical audiences

Contribute to technical documentationarchitecture wikis experiment write-ups and model cards

Growth & Community

Present at internal ML paper reading sessions and science forums

Stay current with state-of-the-art research (NeurIPS ICLR ACL KDD) and bring new ideas to the team

Mentor junior scientists and interns; participate in hiring interviews and debriefs

Publish findings in top-tier venues and file patents where applicable



- 3 years of building models for business application experience
- PhD or Masters degree
- 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

- Experience using Unix/Linux
- Experience in professional software development

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