Quant interviews usually test math (probability, linear algebra, calculus), coding efficiency (Python/C++), brain teasers or logic puzzles, and some finance basics like option pricing or stochastic processes. They want to see how you think, not just what you know.

The recruiting process at Allianz left much to be desired. The questions asked were superficial and lacked depth, with an overemphasis on basic concepts that seemed disconnected from the actual role. The team, while polite and quite accomodating, gave the impression of being disengaged and unenthusiastic about their own work environment. The interview seemed to focus more on justifying simple answers rather than evaluating relevant skills. Many of the questions appeared irrelevant to the position, leaving me wondering about the efficiency of their hiring process. In terms of the role itself, it is far from a true quantitative position. The responsibilities mainly revolve around maintaining and validating existing models, with little to no opportunity for actual development in the machine learning field. This role does not seem to align with the expectations one would have for a position in this domain. Communication was also lacking. Email responses were slow, and the overall process felt disorganized, which gave the impression that the company does not value candidates’ time. Additionally, the compensation offered was below average for this type of role, making it hard to justify the investment of time and effort in pursuing this opportunity.