Excellent place for a career start but don't stay too long - Mitarbeiter (anonym) bei Bloomberg: Mitarbeiterbewertung

2,0
9. Sep. 2018
Mitarbeiter (anonym)
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Pros

Excellent Internship and graduates programmes International environment Internal career moves are encouraged (also because it's the only way to remain in the company longer than 2 years)

Kontras

very little work-life balance: to get ahead the only option is to work long hours on a permanent basis. If you don't (because of personal or health reasons) you will hardly be considered for a promotion or a salary increase. short staffed: most departments are constantly short in staff, putting a lot of pressure on the remaining people to deliver the same results as if the team was fully staffed. weak management in most departments : very few will stand up for their people and work together to complete a project successfully. Most managers are not interested in the day-to-day work. NY centric: all the decisions are taken in headquarters, conference calls with EMEA and APAC are just an fyi. re-orgs are really badly managed and communicated: there is no care for people nor recognition for the work done.

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5,0
8. Juli 2026
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Pros

good pay, great team, lots of experience gained

Kontras

mundane tasks sometimes, can be competitive

4,0
28. Juni 2026
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Geschäftsprognose

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.

Kontras

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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