A lot of pros just a couple of cons - Sales and Analytics Bloomberg Employee Review

4.0
Dec 15, 2015
Recommend
CEO approval
Business Outlook

Pros

- Bloomberg really takes the time to train its staff well which makes it a good place straight out of university especially if your specialism was not finance. - There are some very smart individuals working there from whom you can learn a lot. - Standard 8-6 hours so you know when you will be out the door. - If you are in a team with good managers, then you do feel like something greater - a real family feel (I have experienced this first-hand) - Have never encountered sexism, can progress well as a woman. - Good pay/general benefits and working towards a better bonus structure from 2016 onwards.

Cons

- If you get placed in a team or are in a department with bad/incompetent/micro-managing/ nonsensical-metric-oriented managers then you will feel like another cog in the wheel and be very disheartened. - The standard hours are a little too long and inflexible (but the pros of this still stands). - Some over promising and under delivering with career progression, you are at the mercy of your manager or team leader.

Explore other reviews about Bloomberg

5.0
Jun 30, 2026
Recommend
CEO approval
Business Outlook

Pros

Great compensation, work life balance

Cons

4 days a week on site

4.0
Jun 28, 2026
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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