We propose \textbf{occ2vec}, a principal approach to representing occupations, which can be used in matching, predictive and causal modeling, and other economic areas. In particular, we use it to score occupations on any definable characteristic of interest, say the degree of \textquote{greenness}. Using more than 17,000 occupation-specific text descriptors, we transform each occupation into a high-dimensional vector using natural language processing. Similar, we assign a vector to the target characteristic and estimate the occupational degree of this characteristic as the cosine similarity between the vectors. The main advantages of this approach are its universal applicability and verifiability contrary to existing ad-hoc approaches. We extensively validate our approach on several exercises and then use it to estimate the occupational degree of charisma and emotional intelligence (EQ). We find that occupations that score high on these tend to have higher educational requirements. Turning to wages, highly charismatic occupations are either found in the lower or upper tail in the wage distribution. This is not found for EQ, where higher levels of EQ are generally correlated with higher wages.