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职位Amazon

Data Scientist, WW Ops, FP&A, WW Ops FP&A

Amazon

Data Scientist, WW Ops, FP&A, WW Ops FP&A

Amazon

Bellevue, WA, USA

·

On-site

·

Full-time

·

5d ago

The WW Operations IPAT team is revolutionizing Amazon's financial forecasting through Trend Cast, an innovative, automated, science-based top-down forecast modeling that's transforming how we predict Worldwide Operations costs. This innovation approach leverages key performance indicators to generate automated, transparent forecasts with unprecedented frequency. By moving beyond traditional bottom-up planning, Trend Cast empowers leadership with real-time insights, enabling swift identification of both risks and opportunities.
As our operational scope rapidly expands into Generative AI, we are also building an intelligent, GenAI-powered Finance Knowledge Base to further reduce the workload on finance teams and facilitate leadership’s decision-making process. We are seeking a driven Data Scientist to help build out these advanced analytical and AI-driven solutions.

Key job responsibilities
In this role, you will be a core contributor to our data science initiatives, working at the intersection of traditional machine learning and Generative AI. You will work closely with senior scientists, engineering, and finance stakeholders to translate complex business problems into scalable models. Your work will directly impact our financial forecasting accuracy (Trend Cast) and help develop intuitive, LLM-powered tools that allow finance teams to query, synthesize, and extract insights from our extensive financial knowledge base.

  • A day in the life
  • Develop, train, and evaluate machine learning and statistical models for financial forecasting, ensuring solutions are scalable for large-scale operational challenges.
  • Design and implement Generative AI applications, specifically building and optimizing Retrieval-Augmented Generation (RAG) pipelines and LLM-based agents to power our internal Finance Knowledge Base.
  • Drive modeling projects from data exploration and feature engineering to model deployment and monitoring, working collaboratively with both technical and non-technical stakeholders.
  • Build and maintain robust data pipelines, utilizing distributed computing frameworks to process and analyze petabyte-scale financial and operational datasets.
  • Develop a deep understanding of key business metrics and KPIs, connecting model outputs to actionable levers that inform strategic operational decisions.
  • Collaborate closely with finance, product, and BIE teams to deploy models, gather feedback, and continuously iterate on both forecasting algorithms and GenAI user experiences.

Basic Qualifications

  • 1+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
  • 2+ years of data/research scientist, statistician or quantitative analyst in an internet-based company with complex and big data sources experience
  • 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience
  • Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)

Preferred Qualifications

  • Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
  • Knowledge of statistical packages and business intelligence tools such as SPSS, SAS, S-PLUS, or R
  • Knowledge of machine learning concepts and their application to reasoning and problem-solving
  • Experience with clustered data processing (e.g., Hadoop, Spark, Map-reduce, and Hive)
  • Experience working with or evaluating AI systems
  • Experience applying quantitative analysis to solve business problems and making data-driven business decisions
  • Experience effectively communicating complex concepts through written and verbal communication

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, Bellevue - 108,300.00 - 160,000.00 USD annually

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关于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+

员工数

Seattle

总部位置

$1.5T

企业估值

评价

2.9

10条评价

工作生活平衡

2.8

薪酬

3.7

企业文化

2.5

职业发展

2.3

管理层

2.1

35%

推荐给朋友

优点

Good pay and compensation

Strong benefits package

Flexible scheduling options

缺点

Poor management and leadership

Limited growth and promotion opportunities

High stress and demanding work environment

薪资范围

4个数据点

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份报告

$181,968

年薪总额

基本工资

-

股票

-

奖金

-

$154,672

$209,264

面试经验

10次面试

难度

3.7

/ 5

时长

21-35周

录用率

20%

体验

正面 10%

中性 10%

负面 80%

面试流程

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Loop

6

Team Matching

7

Offer

常见问题

Coding/Algorithm

System Design

Behavioral/STAR

Leadership Principles

Technical Knowledge