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Applied Scientist II, Amazon Payments Team

Amazon

Applied Scientist II, Amazon Payments Team

Amazon

Seattle, WA, USA

·

On-site

·

Full-time

·

1mo ago

Amazon Payments build systems that process payments at an unprecedented scale, with accuracy, speed, and mission-critical availability. We process millions of transactions every day worldwide across various payment methods. Over 100 million customers and merchants send hundreds of billions of dollars moving at light-speed through our systems annually. We are looking for a highly skilled, experienced, and motivated Applied Scientist to innovate and solve complex scientific optimization challenges at a massive scale.

This Applied Scientist role will design and implement state-of-the-art AI prediction, forecasting and optimization models that generate multi-billion dollar predictions of the highest level of visibility and importance for Amazon's Payments and Customer Experience. A successful candidate will be a problem solver who enjoys diving into data, is excited by difficult modeling challenges, and possesses strong communication skills to effectively interface between technical and business teams. You will contribute to the research community by working with other scientists across Amazon and our Payments Engineering as well as by collaborating with academic researchers and publishing papers. You will work closely with Software Development Engineers to invent and construct models on data at massive scale and it is likely that your work will end up in an Amazon product. Finally, you will also have exposure to senior leadership as we communicate results and provide scientific guidance to the business.

Key job responsibilities

As a Applied Scientist you will:

  • Drive collaborative research and creative problem solving.
  • Develop state of the art AI Agents that reduce operations across Payment teams.
  • Lead research on customizing LLMs to incorporate payment context and reduce latency of LLM invocation, reduce hallucinations and compute while maintaining or increasing accuracy.
  • Write production-grade quality code for deploying machine learning and deep learning modes.
  • Constructively critique peer research and mentor junior scientists and engineers.
  • Create experiments and prototype implementations of state-of-the-art AI techniques.
  • Collaborate with engineering teams to design and implement software solutions for science problems.
  • Contribute to progress of the Amazon and broader research communities by producing publication
  • Effectively collaborate in a fast paced environment with multiple teams in large organization (software development, Project Management, Build and Release, etc).

About the team
Amazon Payments Machine Learning team leverages data across the payment lifecycle to build ML capabilities to improve payment-operations' success rates for verification, authentication, authorization, settlement, refund, disbursement etc. The team also leads impactful generative AI initiatives, driving reductions in operations, an improved developer experience, and friction-less client interactions.

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 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
  • 3+ years of hands-on predictive modeling and large data analysis experience
  • 3+ years of building machine learning models or developing algorithms for business application experience

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.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually

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About Amazon

Amazon

Amazon

Public

Amazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.

10,001+

Employees

Seattle

Headquarters

Reviews

2.9

10 reviews

Work Life Balance

2.8

Compensation

3.7

Culture

2.5

Career

2.3

Management

2.1

35%

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Pros

Good pay and compensation

Strong benefits package

Flexible scheduling options

Cons

Poor management and leadership

Limited growth and promotion opportunities

High stress and demanding work environment

Salary Ranges

2 data points

Junior/L3

L2

L3

L4

L5

L6

M3

M4

M5

M6

Mid/L4

Principal/L7

Senior/L5

Staff/L6

Director

Junior/L3 · Data Scientist L4

0 reports

$181,968

total / year

Base

-

Stock

-

Bonus

-

$154,672

$209,264

Interview Experience

10 interviews

Difficulty

3.7

/ 5

Duration

21-35 weeks

Offer Rate

20%

Experience

Positive 10%

Neutral 10%

Negative 80%

Interview Process

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Loop

6

Team Matching

7

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Leadership Principles

Technical Knowledge