A Munich-based industrial client came to us last quarter looking for two roles at once: a machine learning engineer to build forecasting models inside their S/4HANA environment, and a finance controls specialist whose entire job would be evaluating whether those models were still accurate three months after deployment. A Zurich pharmaceutical company made a similar paired request two months later. So did a Frankfurt logistics group.
The reason is practical. Models drift. Data quality degrades. The forecast that worked brilliantly in Q1 starts producing answers in Q3 that nobody can explain to the auditors. CFOs who rushed AI tools into production are now creating oversight roles to catch problems before the external audit does.
Our current specifications ask for candidates who can sit between the technical AI team and the finance leadership, translating model outputs into language that satisfies both the CFO and the external audit partner. The role combines data literacy, financial controls expertise, and communication. Finding people who bring all three is difficult. We ran a search for a Düsseldorf manufacturer last month and presented five candidates over eight weeks before one met the full specification.
For candidates building careers in SAP finance, the premium offers go to people who can explain why a model gave the answer it gave and flag when the answer should no longer be trusted. One candidate we placed in Frankfurt last quarter had spent two years doing SAP configuration, then moved into a data governance role. Her ability to speak both languages got her a salary offer fifteen percent above the published range. Companies want people who can ask the right questions of a model before the auditors ask them first.
Prompted by reporting from ERP Today.