Wolf is sheep clothing work advertising - HR- Representative Bloomberg Employee Review

3.0
22 June 2022
Recommend
CEO approval
Business outlook

Pros

Good work culture at this organization

Cons

This role is not as advertised, it’s low pay and even worse the training is bad and not in depth. You have to take a series of tests you must pass to be in the role! Then they don’t tell you in the interview you will be strapped to your desk all day receiving ticket requests from employees literally every 2 min or less you will get assigned a ticket. The problem with this is by the end of the day you can have 50 tickets per day. If you don’t resolve those tickets you will get the same amount of tickets the next day and so on. The back breaker when you go on vacation there is no one to do your tickets somehow you must do them no matter what. Then you’re rated on a system by your manager who is a do it my way manager, not a good manager at all. Then you also have a phone to go along with the tickets to take employee calls which are considered tickets too. This is a high stress job stay away!

Explore other reviews about Bloomberg

5.0
30 June 2026
Recommend
CEO approval
Business outlook

Pros

Great compensation, work life balance

Cons

4 days a week on site

4.0
28 June 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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