You have to decide what you want before joining this company - Global Data Analyst Bloomberg Employee Review

For the candidates taking interviews or considering the offer of Market Data Analyst or other Global Data related position, I suggest you making up your mind of what you expect to gain from this job. First off, let's begin with daily work. As an analyst in Global Data, it is inevitable for you to take up some manual data entry or maintenance works and the volume depends on which team you will be assigned. For example corporate actions team is well known for it notoriously huge amount of daily work such as resolving helpdesk tickets, clearing workitems from the queue in processing tool etc. Fundamental or Estimate teams will involve less helpdesk ticket resolving but more on value tagging (or correction of value tagging by vendors) from company financial or broker researches. Now for every new hire, they will be labelled as "level 1" for the first 2 years of tenure with the performance metrics focusing on daily operations productivity. After staying for 2 years you will progress into "level 2" with more metrics on projects you lead or initiate. You should have a psychological preparation about the discrepancy between what has been advertised in job description and the actual daily operations. As a rule of thumb, please pay attention to the first few points listed in the job duty section in a job advertisement because usually they account for the majority of you daily tasks. In terms of "interesting" projects leveraging your data analysis and programming skills, well.. you can see them among the bottom of job duty section and so you should know what I mean. Of course if you satisfy with data entry and maintenance experience learnt from this position, I think it is a great job for you referring to my Pros above. However if you aim at sharpening your data analysis skills and developing programming expertise through some data science projects, think twice... In fact, the mismatch of expectation explained above is one of the major reasons why there has been such a high turnover rate in this department, and even more devastating during 2018. Global Data did hire some strong candidates with sound Python/R expertise and data management background. But as the previous reviews said, due to numerous roadblocks including but not limited to: clumsy internal procedures to request access to database or even the installation of latest library; predefined preference (or limitation) of platform where you have to code/develop such as BQL/BQNT/DTP etc, such strong candidates are destined to leave soon out of frustration, disappointment and boredom. Another group of attrition focuses on those who equipped with 3+ years of market knowledge and product specific experience. Internally we refer to "Subject Matter Expert" aka SME. Without these SME who excel at asset classes they cover to co-lead the data analysis, auto news generation or process efficiency projects, the quality of final deliverable could be hideous and sometimes is utterly a joke to our client's eyes. A news article citing the valuation of insurance sector health based on P/E ration could be published, unchecked, unchallenged by the analysts, due to the fact that if there is a SME ever he or she will correct your logic to use P/B since it is kind of a well known industry practice if you ask any client from equity research covering insurance sector! Do not expect you team leader would have more market knowledge nor tech skills than you or any analyst to give you some useful advice, simply thanks to the unique management culture in Bloomberg. While Data emphasizes so much on engaging tech stack, very few, if not none, of the team leaders knows how to code in Python or other languages. Be prepared for yourself to be constantly asked by your team leader during bi-weekly catchup with one FAQ: Is there any project you want to do? I bet you a facepalm if you are conscious enough to ask this yourself: Isn't it supposed to be the TL who should possess in-depth product knowledge and insight to help initiate project ideas and assign to analysts according to their different skill sets, rather than ask analysts to contribute ideas simply because the TL has none? If you read through the long article and arrive at here, I truly appreciate your effort invested in researching this company or global data. All things considered, if you do not mind of steep learning curve and value your learning opportunity and exposure to the true big data analysis and data science, go somewhere else because I am sure there are many great companies out there with a team full of supportive, competent and visionary seniors and managers who truly know what data science is. If you only look for a fresh graduate job with a beat-the-market starting salary, I wholeheartedly suggest you think about your exit strategy starting from as soon as your onboard date. We have long lost the job stability with a clear observation last year about the layoff of a bunch of news reporters and even managers who worked for more than 12-15 years, just before the 10Bln long term bonus payout in this March.