Lazard Freres Review Data
8 total review submissions
|2019||3rd+ Year Analyst||New York||Investment Banking||Group/office is very high quality. People are mostly…||Hours are long, work is expected to be done the day you get…|
|2020||2nd Year Associate||New york||Investment Banking||Good exposure to top clients and lots of responsibility at…||Lean deal teams so hours can be tough sometimes. Technology…|
|2019||3rd+ Year Analyst||New York||Mergers and Acquisitions||The people in my group are super friendly and cool. There…||The workload at an EB like Lazard is insane most of the…|
|2019||2nd Year Analyst||Chicago||Restructuring||Will only speak on my group at my office to prevent any non…||Given COVID and its implications on number of distressed…|
|2019||1st Year Analyst||Los Angeles||Investment Banking||Lazard has always been a great bank in terms of experience…||Certain groups can be more political than others and the…|
|2020||Intern||Houston||Oil & Gas||Lazard has invested significant time into their interns and…||My internship was moved online due to COVID-19. Although…|
|2019||1st Year Analyst||Richmond||Mergers and Acquisitions||Strong analyst training and access to upper management.…||Sometimes it could be a lack of direction for a college…|
|2019||2nd Year Analyst||New York||Mergers and Acquisitions||* Strong deal flow across groups
* Good buyside…
|* Long hours
* Lower compensation than that of…
Overall Ranking is a score from 1 star (very bad) to 5 stars (excellent) generated based on the Company Reviews of current and former employees at this company, taking everything into account.
The number you see in the middle of the donut pie chart is the simple average of these scores. If you hover over the various sections of the donut, you will see the % breakdown of each score given.
The percentile score in the title is calculated across the entire Company Database and uses an adjusted score based on Bayesian Estimates (to account for companies that have few reviews). Simply put, as a company gets more reviews, the confidence of a "true score" increases so it is pulled closer to its simple average and away from the average of the entire dataset.
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