I'm experimenting with bayes rule. I have been invited to last stage interview in highly selective company. I want to use bayes rule to evaluate my chances of getting the post.

Let's define the followings :

$J ={}$ The hypothesis that I get the job  

$I ={}$ Being in the last stage interview

I want to know $P(J\mid I),$ i.e. the likelihood that I get the job given that I'm in last stage interview. According to Bayes rule:
$$ P(J\mid I) = \frac{P(I\mid J) P(J)}{P(I)}$$

My question is how to make sense of $P(I\mid J)$? is it equal to $1$?

Let's say that $P(I)= 0.05,$ on average, from 20 applicants who send their cv, 1 goes to final interview.
For the prior $P(J),$ let's say, based on my previous experiences, from 10 jobs I apply to, I get offered 1 position. Then $P(J)$ is $0.1$

then bases on these $P(J\mid I) = 0.1/0.05 = 2$
ok that's a likelihood, it can be greater than $1.$ But how do I interpret it?
For me, the problem is with $P(I\mid J)$ but I don't know how to fix it? maybe my formulation is incorrect.  

2nd question, for another candidate that has been also invited to the last stage interview, only $P(J)$ changes. is that correct? But $P(I)$ should change as $P(J)$ changes as it can be conditioned on $J.$

Last question, Let's say that I also know from previous experiences that when I get invited to the last stage interview, I get the position 80% of the time. But that's is $P(J\mid I)$ itself. How do I use this information for this company which is more selective (lower $P(I)$). How to incorporate $0.8$ in my prior $P(J)$? obviously I can't say $P(J)=0.8$