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Staff Machine Learning Engineer, Ads Growth

Coupang

Salary not specified
Apr 28, 2025
Seattle, WA, US
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The company is looking to leverage machine learning and data analysis to improve advertiser benefit and expand its ads business to new customers and products.

Requirements

  • Master or Ph.D. degree in quantitative fields, such as computer science, statistics, electrical engineering, mathematics or equivalent.
  • 4+ years (for Ph.D.) or 6+ years (for other degrees) of industry experience working with large-scale, complex datasets to create and optimize machine learning models.
  • Ability and experience to write robust and efficient code using Java, Python, etc for model prototypes and working with software engineers for productionizing them.
  • Ph.D. in quantitative fields is strongly preferred.
  • 3+ year experience with advertising or ecommerce industry.

Responsibilities

  • Work with product team, sales team, and other stakeholders to formulate the business problems into machine learning and data analysis projects.
  • Design features and build large-scale machine learning models and systems.
  • Perform statistical analyses to improve advertiser benefit and our ads revenue.
  • Find new opportunities to expand our ads business to new customers/products with data and machine learning model supports.
  • Solve ambiguity and act as consultant in ML model related problems across team.
  • Mentor the junior team members.
  • Effectively collaborate with other roles, including machine learning engineers, data scientists, economists and software developing engineers, to lead projects within or across team.

Other

  • Strong problem-solving skills.
  • Track record of diving into data to discover hidden patterns and solving problems with data science, launching successful features and improving business metrics.
  • Strong ownership and ability to lead project end to end;
  • Good communicator, both to technical and business audiences.
  • Ability to work in multicultural, cross functional team, and to deal with ambiguity in business and technology.