HN is understandably filled with programmers who value programming above all else and believe domain knowledge can be picked up quickly. It would be true for someone hired as a programmer who implement a published algorithm or port existing software to newer systems or different platform; in other words, improving existing systems. I believe it requires equal depth if not not more for domain specific knowledge to innovate in non-computational fields. For my field of bioinformatics/computational biology, many open questions cannot be answered because of computational hurdles. Reading textbooks won't lead anyone to them, nor would reading survey papers. It requires expert knowledge in both biology and computer science to even know what is feasible and interesting. We want to find the driver mutations for cancer, discover mechanism of gene regulation, uncover subtle interaction of heredity and environment. Computation is intimately involved because observation is indirect, noisy, and massive. Only with knowledge in both fields can answer questions that domain experts care about but cannot do because of computational/quantitative obstacles.

I find researchers (I am in academia) in both fields cross-talk too much. Computational/quantitative experts try to shoe-horn biological questions into their small corner of research area, while biologists don't care about computational efficiency, statistical robustness/efficiency, and the mathematical implication of their simple methods; no one cares about software quality. This is unfortunate. It is also unfortunate hiring committees do not look favorably towards multi-disciplinary researchers because one is not a trained geneticist/cancer researcher. I definitively do not encourage computer scientists go into support roles in academia; they are poorly paid and get little respect.