Run - As far as and as soon as you can - Mitarbeiter (anonym) bei Bloomberg: Mitarbeiterbewertung

1,0
18. Juli 2011
Mitarbeiter (anonym)
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CEO-Befürwortung
Geschäftsprognose

Pros

Nice building New york location Good food Financial information access Good trainings but mostly wasteful because of poor management in real teams

Kontras

Year 1- You will learn quite a lot new stuff(on your own), work on all challenging fast paced stuff .You might still end up working under managers who have less educational background, skills and outside bloomberg experience who will actually be insecure of you and will try to make your eval bad for that. Believe me, they don't care at all about product in all this. I also think this is the case mostly because these tls are pretty aimless themselves in their goals. They do bachelors in some totally different field, join financial firm with software development for money. Looks like they are not at all clear about their aim in all this and not true to their profession. Year 2- Its still ok. you will still get to learn something. Year 3- Lot of repetitive, mechanical work Year 4 and onwards - If you still stay here, you will become completely useless for software development in outside world.

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5,0
30. Juni 2026
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CEO-Befürwortung
Geschäftsprognose

Pros

Great compensation, work life balance

Kontras

4 days a week on site

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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