Amazon

DESCRIPTION

We are seeking a passionate and strategic Principal Applied Scientist to create machine learning solutions and models for providing better value to Amazon's customers. This team offers a unique opportunity to make a significant impact on the customer payment experience and contribute to the design, architecture, and implementation of novel products. You are a hands-on scientific leader. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.

In this role, you will be responsible for building AI/ ML strategies, partner closely with a team of scientists and software developers, and develop novel algorithms and modeling techniques.

Key job responsibilities

• Invent, implement, and deploy novel machine learning algorithms and systems.
• Spearhead the design, development, and deployment of advanced analytics models, machine learning algorithms, and predictive modeling frameworks to derive actionable insights from large and diverse data sets.
• Collaborate cross-functionally with stakeholders from business, engineering, and subject matter experts to define project requirements, prioritize initiatives, and ensure alignment with organizational goals.
• Define key research directions, conduct rigorous experiments, publish results, and ensure that research is translated into practice.
• Mentor and guide a team of data/applied scientists, fostering a culture of continuous learning, innovation, and knowledge sharing.
• Serve as a subject matter expert, providing technical guidance and thought leadership on the application of data science principles to address complex business problems and drive strategic decision-making.
• Serve as science advisor for payments leadership team and a collaborative partner within the tech principal community within Amazon Payments

BASIC QUALIFICATIONS

• MS in Mathematics, Statistics, Machine Learning, or a related quantitative field
• 10+ years of CS, CE, ML or related field experience
• 8+ years of building models for business application experience
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience in genAI and LLMs applications
• Experience in software development skills
• Excellent skills in problem solving, programming, and computer science fundamentals.
• Excellent communication skills.

PREFERRED QUALIFICATIONS

• PhD in Mathematics, Statistics, Machine Learning, or a related quantitative field
• Ability to communicate effectively across multiple organizations in the company.
• Experience with use of Amazon Web Services and cloud computing technologies

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $179,000/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.