Data Scientist (Banking)

The modelling is the visible part; the work is data quality, definitions that differ between systems, and the long argument about whether a result is real. Anything that touches a customer outcome will be validated independently and questioned by a regulator, which changes how you build.

Statistics, mathematics, engineering and economics backgrounds, and analysts moving up from reporting seats.

These are the shapes the round takes, not a question bank — banks phrase them differently and the wording is never the point.

What the panel is listening for. Scepticism about your own output, and whether you can explain a model to the person whose decision it will change.

A credit model is a compressed claim about how borrowers behave, so the modellers whose work survives validation are the ones who have looked at real files and know what the data is standing in for.

That is the general case as well as this one. A bank's technology is not ordinary technology and its finance is not ordinary finance, and the second and third lines of defence — risk, compliance, financial crime, model validation and internal audit — recruit heavily out of the functions, taking the people who can already read a business. The case drills are free and are the cheapest way to build that, whichever column you sit in.