Applied Scientist II, International and Emerging Stores Payments

Amazon Inc.; Seattle (WA); Vor Ort; USD 140.000 - 190.000; Vollzeit

Zusammenfassung

Amazon Inc. is seeking an Applied Scientist II in Seattle to design, build, and deploy machine learning and generative-AI solutions that remove payment barriers and expand financial access for customers worldwide.

You will work at the edge of research and production, collaborating with engineers, scientists, and economists to bring models to real-time, low-latency systems, run large-scale experiments, and publish findings.

Qualifikationen

• 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 programming or scripting language like Python, Java, C or C++.
• Experience in solving business problems through machine learning, data mining and statistical algorithms.

Aufgaben

• Design, build, and deploy machine learning and deep learning models, and deploy production-ready solutions to complex customer and business problems.
• Conduct rigorous scientific research: form hypotheses, run experiments, evaluate model performance, and iterate on approaches within your domain of expertise.
• Write clean, maintainable code that integrates smoothly into larger systems and meets a high bar for operational excellence.
• Partner with engineering to take your models to production, and with scientists across related payments ML teams to solve shared, complex problems.
• Contribute to team roadmaps, priorities, and technical direction; mentor peers and provide constructive feedback on research and code.
• Publish at peer-reviewed venues and file patent applications where appropriate.

Kenntnisse

ML model development

Python/Java/C/C++

Data mining

Statistics

Ausbildung

PhD

Masters in CS/CE/Math/Engineering

Tools

Python

Java

C/C++

Jobbeschreibung

Do you want to translate innovative science into products that expand financial access for millions? At International and Emerging Stores Payments (IESP), we build payment solutions for communities and geographies that Amazon's selection and prices do not yet reach because of payment barriers. These markets make up roughly 80% of the world's countries and 85% of its population.

As an Applied Scientist II on our team, you will design, build, and ship machine learning and generative-AI solutions that remove those barriers and advance financial inclusion, working at the intersection of research and production alongside scientists, engineers, and economists. If you are a curious, self-motivated researcher who turns ambiguity into clarity, we would like to hear from you.

What you will work on:

• Build models that recommend the most suitable payment method for each customer-for example installments, cards, or local payment options-and identify the right customers to reach, balancing customer preference, cost, and risk.
• Take models to production behind a real-time, low-latency personalization layer that serves live shopping traffic at scale.
• Build multi-agent generative-AI systems that turn unstructured inputs-customer feedback, documents, and operational data-into reliable, decision-ready insight, with verification that guards against model hallucination.
• Help customers discover and adopt installments and alternative payment methods.
• Design and analyze large-scale online experiments and causal measurement to validate real customer and business impact.

Key job responsibilities

• Design, develop, and evaluate machine learning and deep learning models, and deploy production-ready solutions to complex customer and business problems.
• Conduct rigorous scientific research: form hypotheses, run experiments, evaluate model performance, and iterate on approaches within your domain of expertise.
• Write clean, maintainable, and efficient code that integrates smoothly into larger systems and meets a high bar for operational excellence.
• Partner with engineering to take your models to production, and with scientists across related payments ML teams to solve shared, complex problems.
• Contribute to team roadmaps, priorities, and technical direction; mentor peers and provide constructive feedback on research and code.
• Publish at peer-reviewed venues and file patent applications where appropriate.

A day in the life

You will start your day reviewing experiment results and refining model architectures based on new insights. Throughout the day you will alternate between writing research code, analyzing data, and discussing technical approaches with teammates, working closely with engineering to build your ideas and take them to production. You will regularly present findings and propose new ideas, and you may write research papers and submit patent applications.

About the team

We are the science team for International and Emerging Stores Payments. We build solutions that directly shape how customers pay and that drive conversion across Amazon's fast-growing international markets-and there is no shortage of hard science and engineering problems, since these markets span most of the world's countries and the majority of its customers. We value scientific rigor, creative problem-solving, and a genuine commitment to learning and growth. If you are excited about building impactful solutions and advancing financial inclusion through applied science, we would love to have you join us.

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 programming or scripting language like Python, Java, C or C++
• Experience in solving business problems through machine learning, data mining and statistical algorithms

Preferred Qualifications:

• Ph.D. in computer science, engineering, mathematics or equivalent, or Master's degree and 1+ years of a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science experience
• Experience in building machine learning models for business application
• Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients
• Experience working with big data, machine learning and predictive modeling
• Experience providing and effectively communicating strategic and tactical recommendations based on data
• Experience in solving complex business challenges by delivering accurate and timely financial models, analysis, and recommendations that have a proven impact on business (e.g., financial savings, operational improvements, or customer benefits), or experience communicating results to senior leadership
• Experience creating and delivering written and oral communications for technical and non-technical audiences
• Experience in data science, b