You aren't applying Bayes correctly. As a general guide, you need to add the word "given" to your problem statement, assign two "situation A" and "situation B" variables, and then work the math.
For example, assigning variables:
A = You died
B = You're skydiving at that particular dropzone
Then:
"What is the risk of mortality GIVEN that I am skydiving at this drop zone?" (P(A|B))
"What's the chance that I'm skydiving at this dropzone, given the fact that I died?" (P(B|A))
"What's the risk of my mortality while skydiving?" (P(A))
"What is my probability of skydiving at this dropzone?" (P(B))
P(A | B) = (P(B | A) * P(A)) / P(B)
So you calculated P(A | B) in a non-Bayesian way, without finding out the other information to calculate it using Bayes, and then stopped there, like most people do. This is why Bayes is often difficult for people to understand and apply correctly, and, honestly, it's probably not the equation you want for the situation you're looking at.
Another approach -- and this seems to be the one you want -- is to calculate a 95% confidence interval using a binomial distribution, to find out if their statistics are really anomalous. Death is a relatively rare event, and, even if they're distributed perfectly randomly, you'll find odd-looking clusters here and there.
To figure out if it's anomalous, many people would use the normal approximation to the binomial confidence interval, which would be wrong -- the probabilities of death are so relatively tiny, that they can't be approximated normally (rule of thumb is P(A) x P(not A)x sample size > 5 to use the normal distribution, which this fails), so we need to do an exact calculation. I've done this by hand before, but it's a pain in the butt. That's why we have calculators!
http://epitools.ausvet.com.au/content.php?page=CIProportion (If you don't trust it, you can use another one)
You enter your numbers:
sample size = 75000
number of deaths = 2
This gives the exact binomial confidence interval as:
[3.23e-06, 9.633e-05]
This means that their actual death rate could be anything from
.00000323 (that's 3 deaths per million) to .000096 (that's nearly 100 deaths per million)
Clearly, you cannot say with any meaningful level of confidence whether or not this drop zone is safer, or less safe, than average. Sorry!
Edit: In my Bayes example earlier, I realized that you could actually use it in an interesting-ish way (I guess?) to find out an unknown: "What are the odds that I was skydiving at this particular dropzone, given that I died skydiving?"
So:
A = You're skydiving at that particular dropzone
B = You died :(
P(A | B) = (P(B | A) * P(A)) / P(B)
We know that:
P(B | A) = 2/75000 = .0000267
P(A) = their skydives / all skydives = (75000 / 3,300,000 x 15) = 0.0015
Note: I found out that there were 3.3 million skydives in the US in 2012, so, let's extrapolate that out 15 years as a rough approximation, and limit us to just the US.
P(B) = .000009
Then:
P(A | B) = (.0000267 * .0015) / .000009 = 0.00445
So, if you died, then the probability that you were skydiving at that dropzone is 0.4%!
Note though, that there's some uncertainty in the calculation of P(B | A) (the probability of dying while skydiving at that dropzone) which you need to use confidence intervals, above, to actually figure out. Anyway, I certainly clarified some of my thoughts while writing this, and I hope that it helps you too, figuring out when to use confidence intervals, and when to use Bayes, and why it matters!