Private Equity Summer Analyst

Status
Group/Division/Type
Generalist
City
Los Angeles
Interviewed
November 2014
Overall experience
Positive
Difficulty
Average

General Interview Information

Outcome
No Offer
Interview Source
College / University / On Campus Recruiting
Length of Process
1-2 months

Interview Details

What did the interview consist of?
Phone Interview
Please describe the interview / hiring process.
There was an initial 30min phone screen of most qualified candidates. From this pool, several candidates were selected for in-person on-campus interviews. From this a few more were selected for an on-site superday in LA.

The phone interview was pretty straightforward and brief. Started with the classic "tell me about yourself" then "why did you apply". Then a significant discussion regarding an LBO project I worked on and put on my resume. My interviewer focused on only my contributions to the project, and thus there weren't any high level LBO modeling questions.
What were the most difficult or unexpected interview questions asked?
Tell me about the Precedent Transactions and Comparables analysis you did?

Walked through how I defined the universes (focused on similar size and same industry firms), what multiples I used (EV/EBITDA is most important by far), and the conclusions that can be drawn from the valuation.
Would there be any reason that a firm with low Fixed Assets might be a good target for an LBO?

He asked this after I described the target firm as being unattractive because of its low fixed assets (which isn't 100% correct). The answer is that this is a double-edged sword. A firm with high-fixed assets can use those assets to secure more (cheap) bank debt, but at the same time those firms often have to make more capital expenditures for maintenance. Lenders will realize this and will factor it into their credit analyses.

Overall Company Rankings

  • 5 Stars
  • 4 Stars
  • 3 Stars
  • 2 Stars
  • 1 Star
Overall Ranking

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.

N/A

Career Advancement Opportunities

Career Advancement Opportunities 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.

N/A

Senior Management

Senior Management 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.

N/A

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