  Mathematical modelling of unemployment dynamics attempts to predict the
probability of a job seeker finding a job as a function of time. This is
typically achieved by using information in unemployment records. These records
are right censored, making survival analysis a suitable approach for parameter
estimation. The proposed model uses a deep artificial neural network (ANN) as a
non-linear hazard function. Through embedding, high-cardinality categorical
features are analysed efficiently. The posterior distribution of the ANN
parameters are estimated using a variational Bayes method. The model is
evaluated on a time-to-employment data set spanning from 2011 to 2020 provided
by the Slovenian public employment service. It is used to determine the
employment probability over time for each individual on the record. Similar
models could be applied to other questions with multi-dimensional,
high-cardinality categorical data including censored records. Such data is
often encountered in personal records, for example in medical records.
