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      Momentum matters - Global Data Analyst Bloomberg Employee Review

      4.0
      Jun 9, 2020
      Global data analyst
      Recommend
      CEO approval
      Business Outlook

      Pros

      - If you receive good projects, you can learn a lot, then you will be given even better projects. - Can work with News / Sales should you interested in connecting with people - Have training on programming / data analysis if you did not have prior knowledge - No racism is allowed, which is a big pros nowadays?

      Cons

      - There aren't many good projects - Management does not do a good job in giving out equal opportunity - Some analysts with relatively worse skills could be promoted which hammer the morale a little bit. - Very self-driven environment, if you did nothing, you will feel isolated instantly and no one would really care.

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      5.0
      Jul 8, 2026
      Finance analyst
      Recommend
      CEO approval
      Business Outlook

      Pros

      good pay, great team, lots of experience gained

      Cons

      mundane tasks sometimes, can be competitive

      4.0
      Jun 28, 2026
      Data management professional internship
      Recommend
      CEO approval
      Business Outlook

      Pros

      Opportunities to do lots of work with data and finance to apply knowledge in both programming and Subject-Matter Expertise (SME). Excellent Work-Life Balance (WLB) and extremely welcoming culture. You can reach out to anyone for help or just to talk, and they will get back to you (although management does require more scheduling in advance). Generous compensation (good wage) and benefits, including housing for interns. If you heard the rumors that the Bloomberg Princeton office has a great Bloomberg Pantry (read: company-provided breakfast and lunch), the rumors are true.

      Cons

      Not the place for those looking for cutting-edge AI. The company is not as fast with AI as the company prioritizes reliability and accuracy above all, and much of AI is not at an acceptable threshold for management to be willing to take that risk with financial data (at least in 2026). You may get a project to automate menial processes, which is really cool, but that tends to involve actually doing the menial processes, which feels unproductive. Princeton office is good but New York is considered preferable. Coworkers are not very reachable outside of work hours. Compensation is low in Data compared to Software Engineers.

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