Pros
Prestige. Interesting, Hardworking colleagues. Diverse working environment. Great Location. Perks. Good Salary, and benefits package. Good Entry Level training program
Kontras
The management did not give me adequate advice, or guidance, rather they found that they were dissatisfied and they let me go.
Pros
great benefits- 6 month parental leave, top notch medical /dental & vision with no premiums for the whole family, 3-5 weeks of vacation (based on tenure), free snacks and beverages in every office good internal/external help system- any minor workplace issues & questions get resolved really fast, sometimes within minutes to few hours. example: wonky workchair or keyboard can be replaced within less than an hour, computer/mic/camera issues get resolved in minutes Open floor plans and easy access to teams and people across all divisions True diversity- ppl from all walks of life and backgrounds and with different skillsets and levels of education which brings ranges of perspectives
Kontras
archaic tech infrastructure. everything has to be build on top of the code from 1980 and multiple mismatched apps bundled together. to derived simple consensus sometimes takes several minutes and 4-5 different apps/screens. sales has become less about selling and more about making numbers look good, heavily window dressing and internal political game since no one gets commissions which then encourages this types of behaviors.
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.