Company

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addressAddressSeattle, WA
CategorySales/marketing

Job description

We live in an age where algorithms make a large volume of decisions for us. Algorithms necessarily work on patterns. Patterns we like tend to be labeled best practices, and patterns we don’t like are labeled biases. We are committed to investing in developing a philosophical and sociological understanding of the patterns we perpetuate, and which patterns we need to review due to a conflict with our values. We believe that this is an important responsibility. To inquire into this area, we are looking for candidates who are rooted at the intersection of humanities and technology. This role will require you to work closely with cross-functional teams such as Machine Learning, Analytics, Engineering, Product Management, Product Marketing, Legal, Fraud, and Finance. In this internship, you will study the evolution of patterns in Commerce broadly, and more specifically in e-Commerce, and then situate our work in that context to provide insight and awareness on patterns and how they intersect with our values.You’ll love it here if you thrive in environments where introspective thought is valued just as highly as collaboration. Where quality matters over quantity. Where liberal arts meets technology.

Request

  • You are a great match, if you:
  • - Are training in both liberal arts and technology. The ideal candidate will have the ability and interest to write thoughtfully about the historical evolution of a field while showing an appreciation for deep technical nuances.
  • - Have an intuitive understanding of optimization methods (data science / decision analysis / industrial engineering) and also basic familiarity with M/L distinctions (classification models and how they are evaluated).
  • - Love writing to express yourself and believe that writing is an art form.
  • - Have a learn-and-grow mentality and want to be challenged in a nurturing and supportive environment.
  • - Have an exposure to Decision Analysis (Applied Decision Theory).
Refer code: 8183523. Students - The previous day - 2024-02-09 21:51

Students

Seattle, WA
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