Applied Scientist — Marketing ML & Product Discovery

Amazon Inc.

Seattle (WA)

On-site

USD 143,000 - 193,000

Full time

7 days ago
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Benefits offered by this job

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

Job summary

Amazon.com Services LLC in Seattle, WA seeks an Applied Scientist to help customers discover popular and relevant products. You will help design and deploy data-driven ML solutions that power recommendations and marketing campaigns globally.

You will work across engineering and business teams to translate data insights into impactful customer experiences, taking ownership from model design through production deployment and ongoing optimization.

Qualifications

  • Patents or publications in top-tier conferences/journals.
  • 3+ years building models for business applications.
  • PhD, or Master’s degree and 4+ years in CS/CE/ML or related field.
  • Proficiency in Java, C++, Python or related language.
  • Experience with algorithms, data structures, parsing, numerical optimization, data mining, parallel and distributed computing.

Responsibilities

  • Create and build critical services that automatically generate, target, and optimize cross-category marketing and merchandising.
  • Design, develop, test, and deploy data-driven, scalable ML solutions for product recommendations.
  • Tackle hard problems, decompose them, and make decisions affecting algorithms and system design.

Skills

Patents/publications
Modeling for business applications
CS/CE/ML degree
Programming in Java, C++, Python
Algorithms and data structures

Education

PhD or Master’s in CS/CE/ML or related

Job description

Amazon.com Services LLC in Seattle, WA seeks an Applied Scientist to help customers discover popular and relevant products. You will help design and deploy data-driven ML solutions that power recommendations and marketing campaigns globally.

You will work across engineering and business teams to translate data insights into impactful customer experiences, taking ownership from model design through production deployment and ongoing optimization.

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