Senior Applied Scientist, Ads Measurement & Causal AI

Amazon

Seattle (WA)

Presencial

USD 167.000 - 226.000

Jornada completa

Hace 12 días
Generador de candidaturas

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Ventajas ofrecidas por este puesto de trabajo

RSUs
Health insurance
Paid time off

Descripción de la vacante

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining advertising through generative AI technologies, changing how customers discover products and engage with brands across Amazon.com and beyond. We combine human creativity with artificial intelligence to improve every stage of the advertising lifecycle, from ad creation and optimization to performance analysis and customer insights.

You will work with product, engineering, and business partners to translate complex findings

Formación

  • 3+ years of building machine learning models for business application experience.
  • PhD, or Master's degree and 6+ years of applied research experience.
  • Experience programming in Java, C++, Python or related language.
  • Experience with neural deep learning methods and machine learning.

Responsabilidades

  • Develop methods to understand how traffic sources, customer journeys, and onsite experiences drive purchasing and revenue, using experimentation, causal inference, and machine learning to separate correlation from true business impact.

Conocimientos

Machine learning
Java
C++
Python
Neural networks

Educación

PhD or MS with 6+ years applied research

Herramientas

R
scikit-learn
Spark MLLib
MxNet
TensorFlow
NumPy
SciPy
Hadoop
Spark

Descripción del empleo

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining advertising through generative AI technologies, changing how customers discover products and engage with brands across Amazon.com and beyond. We combine human creativity with artificial intelligence to improve every stage of the advertising lifecycle, from ad creation and optimization to performance analysis and customer insights.

You will work with product, engineering, and business partners to translate complex findings

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