Data Scientist II, Brand Experience Professional

6 days ago


Seattle, Washington, United States Amazon Full time

At Amazon, we are committed to creating innovative solutions that drive business growth. As a Data Scientist II, Brand Experience Professional, you will play a critical role in identifying data-driven insights and opportunities to improve brand success.

The Brand Experience (BX) organization creates, builds, and leads innovation for brand owners worldwide. Our portfolio of tools enables brands to tell their unique stories, optimize content through experimentation, solicit reviews, and engage with customers across various channels.

We are seeking a skilled Data Scientist to lead us in solving complex problems and collaborating with engineering, research, and business teams for future innovation. Key job responsibilities include:

  • Building, validating, and scaling analytic solutions using state-of-the-art tools based on large datasets
  • Conducting statistical/machine learning analyses or designing experiments to measure the value of the business and its features
  • Partnering closely with business, finance, science, and tech partners from ideation to implementation of production solutions
  • Independently identifying new opportunities for leveraging economic insights and models that drive a better understanding of the brand experience business
  • Writing technical white papers and business-facing documents to clearly explain complex technical concepts to partner teams and leaders

Estimated salary range: $125,500 - $212,800 per year, depending on location and experience.

Basic Qualifications:

  • 2+ years of data scientist experience
  • 3+ years of data querying languages (e.g., SQL), scripting languages (e.g., Python), or statistical/mathematical software (e.g., R, SAS, Matlab) 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
  • Knowledge of relevant statistical measures such as confidence intervals, significance of error measurements, development, and evaluation data sets


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