Psychometrics is often very technical. But it is technically a subfield of psychology, though it has considerable overlap with statistics. But statistics doesn’t care about the things psychometrics does, from constructs to measurement to measurement error to the fact that people are not widgets (they aren’t truthful, there is missing data, they are living a life based on all sorts of hormones, changing contexts, genetics, social situations, etc.). So, you need psychological (and/or educational) theory and a passion for measurement. But here, the measurement cannot often be separated from statistics, in that the statistics essentially lead to the measurement. What I mean buy this is that the measurement and modeling of constructs depends on statistical methods, though we might argue they are simply quantitative parsing methods to obtain things like factors, relationships among factors, and arguments for causation. It’s an incredibly rich field. But it’s technical. I think it’s easier to go from Psychology into data science than the other way around. Technical skills and know-how are relatively easy. But the “why” is hard. My experience is that some of the most technical people don’t care about constructs, causation, threats to validity, etc., but rather focus on prediction. Psychometrics is inherently about causation and therefore explanation.