Amazing company curtailed by poor lower management (Global Data) - Global Data Analyst bei Bloomberg: Mitarbeiterbewertung

4,0
10. Okt. 2018
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Geschäftsprognose

Pros

The company places significant value on those who bring significant value...if you are allowed to do so by your manager. Skills such as VBA, Python, and resiliency are treasured within Bloomberg's Global Data division. You are exposed to every level of investor from back office analyst to CEO. It looks great on a resume.

Kontras

Global Data in particular needs to be completely gutted of the awful talent that is leading it on almost every level. The organization chart has become a mess, and people who should be leading departments are suppressed by awful managers who are in their positions because of how long they have been there and who they have befriended. This goes for many, many Team Leaders too. Your experience at Bloomberg will be significantly impacted based on who your initial Team Leader is.

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5,0
6. Mai 2026
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CEO-Befürwortung
Geschäftsprognose

Pros

Great place to work if you are looking for work life balance

Kontras

The data department has very limited growth opportunities

4,0
28. Juni 2026
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CEO-Befürwortung
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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