Summer Intern - Data Science

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The Trade Desk

πŸ“Internship - China

Job highlights

Summary

Join a dynamic, globally-connected team at The Trade Desk to contribute to the development of new and existing data products as a Data Science intern. You will have the opportunity to own a meaningful project, receive one-on-one mentorship, and collaborate with a team where you will also interact closely with our product, trading, and engineering teams to oversee the implementation of your projects.

Requirements

  • Are enthusiastic to learn (and teach) new technologies, machine learning techniques, and advancements in the AdTech industry
  • Have an abundance of intellectual curiosity
  • Have a deep understanding of statistical machine learning and optimization techniques
  • Want to work with big data technologies such as Spark and AWS
  • Have experience in at least one programming language (Python, R, Java, Scala, etc.) and are comfortable with SQL by the start of the internship
  • Are able to deliver a convincing story, derived from data, to both technical and non-technical audiences
  • Are enrolled in a Bachelor's, Master's, or PhD degree program
  • Have an expected graduation date between Autumn 2025 and Summer 2026

Responsibilities

  • Own a meaningful project
  • Have the opportunity to contribute to the development of new and existing data products, experiencing firsthand the day-to-day life of a professional data scientist
  • We believe strongly in ownership at The Trade Desk, and we are committed to you having something substantial to show for your time here that will make a positive impact for the team
  • Receive one-on-one mentorship from an experienced Data Scientist and members of the Data Science team
  • Collaborate with a globally-connected team where you will also interact closely with our product, trading, and engineering teams to oversee the implementation of your projects

Benefits

This position will be based out of our Shanghai office. You will be available to work in-office at least 3 days a week (Tuesday, Wednesday, Thursday)

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