Given time series data, how to model the frequency of someone changes his job?
A simple and perhaps somewhat naive approach would be to assume that a person changes jobs at a constant rate and that previous job changes have no influence on future ones. Under these assumptions you could model the job changes as a Poisson process and estimate the rate parameter using MLE (http://en.wikipedia.org/wiki/Poisson_process and http://en.wikipedia.org/wiki/Poisson_distribution).

Of course one should explore how well these assumptions hold in the data. To do this, you could study whether or not job changes are independent of one another by computing the correlation between events at various lags (http://en.wikipedia.org/wiki/Correlation). You could also plot the distribution of time between job change events. If the process is Poisson-like then you should observe little to no correlation between events at any number of lags and the distribution of time between job change events should be exponentially distributed (http://en.wikipedia.org/wiki/Exponential_distribution).