Many jobs are centered around risk management.  If you’re looking through job postings, of course, you’ll see lots of jobs whose purpose is to make sure that nothing bad happens – the equivalent of locking the doors and closing the windows.  More interesting from a statistical perspective are the jobs that assume that bad things will happen, and try to optimize and manage exposure. As far as leveraging the power of statistics and analytics, there are two strands, one leveraging skills from operations research and the other from predictive analytics:

– Quantitative assessment of system or group risk via Monte Carlo simulation

– Use of machine learning (predictive modelling) to predict risk at the transaction or event level

The assessment of risk through simulation is a long-established business – the finance and insurance industries are built on this foundation.  The overall performance of a portfolio of projects, each with an estimated probability of success, can be estimated through simulation (for example, see our course Financial Risk Modeling).

Machine learning and artificial intelligence, by contrast, operates at the individual level – will a particular transaction, for example, prove fraudulent.

Most job postings that you encounter are of the “lock the doors and windows” variety (watch for terms like compliance, controls, operational, etc.).  The more statistically-oriented risk analysis jobs will usually refer to software, typically Excel; watch for the term “risk-modeling.”