Description

Amazon Japan Store Tech (JST) Science team serves as the core science division of JP Store Tech, with the vision to enable and accelerate the best-in-class CX through state-of-the-art machine learning technologies. This team owns the science vision definition, science roadmap planning, and science solution delivery in key business areas in Japan including Search, Customer Growth and Engagement, Personalization and Delivery.

As a Principle Applied Scientist, you will lead the team to create the science long-term vision and roadmap across multiple business domains. You will lead the design, implement and deliver models on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times.

Key job responsibilities

• Initiate and drive the design and delivery of science solutions or systems that address significantly large or endemic customer and business problems, and directly impact the goals of your entire organization, and possibly others.

• Drive the scientific vision of the teams in your organization and you advise and influence its technical leadership on scientific challenges pertaining to your areas of expertise.

• Lead significantly complex science and design reviews, aligning teams across your organization around coherent algorithmic strategies.

• Recognized by internal and external peers as a thought leader in your area(s) of scientific expertise. You publish your results at top academic conferences and engage with the academic community.

Your benefits include:

• Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers

• The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems with tangible customer impact

• Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goals

We are open to hiring candidates to work out of one of the following locations:

Seattle, WA, USA

Basic Qualifications

• 10+ years of building models for business application experience

• PhD, or Master's degree and 10+ 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 with state-of-the-art modeling algorithms such as LLM, generative AI.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $159,100/year in our lowest geographic market up to $309,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.