Data Scientist II, Selection Growth Science

Amazon

Seattle (WA)

On-site

USD 136,000 - 184,000

Full time

12 hours ago
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Benefits offered by this job

Health insurance
401(k) matching
Paid time off
Parental leave

Job summary

Amazon.com LLC in Seattle, WA seeks a Data Scientist II to advance seller prioritization, analytics, and growth strategies for Selling Partner customers. You will collaborate with engineering, research, and business teams to deliver impactful, seller-centric insights and scalable models for production use.

The role requires deep business domain understanding, hands-on ML expertise, and demonstrated ability to communicate complex results to diverse audiences.

Qualifications

  • 3+ years of data querying languages (e.g. SQL) or scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab) experience.
  • 2+ years of data scientist experience.
  • 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience.
  • Experience applying theoretical models in an applied environment.

Responsibilities

  • Identify opportunities to improve SP growth and translate those opportunities into science problems via principled GenAI solutions.
  • Design and execute roadmaps for complex science projects to help SP have a delightful selling experience while creating long term value for our shoppers.
  • Work with our engineering partners and draw upon your experience to meet latency and other system constraints.
  • Be responsible for communicating our science innovations to the broader internal & external scientific community.

Skills

SQL
Python
R
Matlab
SAS
ML modeling

Education

PhD in computer science/math/statistics/ML

Tools

GenAI tools
ML frameworks

Job description

Description

Join us in the evolution of Amazon’s Seller business! The Selling Partner Selection Succ organization is the growth and development engine for our Store. Partnering with business, product, and engineering, we catalyze SP growth with comprehensive and accurate data, unique insights, and actionable recommendations and collaborate with WW SP facing teams to drive adoption and create feedback loops. We strongly believe that any motivated SP should be able to grow their businesses and reach their full potential supported by Amazon tools and resources.

We are looking for a Data Scientist II to work on our seller prioritization to improve our SP growth strategy and drive new seller success. As a successful data scientist on our talented team of scientists and economists, you will leverage the latest technology to solve complex problems, and collaborate with engineering, research, and business teams to deliver seller‑centric experience on behalf of sour sellers. You need to have deep understanding on the business domain and have the ability to connect business with science. You are also strong in the latest technology and scientific foundation with the ability to collaborate with engineering to put models in production to answer specific business questions. You are an expert at synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication. You will continue to contribute to the research community, by working with scientists across Amazon, as well as collaborating with academic researchers and publishing papers (www.aboutamazon.com/research).

Key job responsibilities
  • Identify opportunities to improve SP growth and translate those opportunities into science problems via principled GenAI solutions.
  • Design and execute roadmaps for complex science projects to help SP have a delightful selling experience while creating long term value for our shoppers.
  • Work with our engineering partners and draw upon your experience to meet latency and other system constraints.
  • Be responsible for communicating our science innovations to the broader internal & external scientific community.
Basic Qualifications
  • 3+ 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 scientist experience
  • 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
  • Experience applying theoretical models in an applied environment
Preferred Qualifications
  • PhD in computer science, mathematics, statistics, machine learning or equivalent quantitative field
  • Experience in a ML or data scientist role with a large technology company
  • Demonstrated experience leveraging generative AI tools to enhance workflow efficiency and productivity, with the ability to craft effective prompts and critically evaluate AI-generated outputs in a professional setting
  • Experience identifying opportunities to integrate AI solutions into products and services to drive business value

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.

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
  • parental leave
  • Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 136,000.00 - 184,000.00 USD annually

Company

Amazon.com LLC

Job ID: A10479600

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