Prediction is a key component of financial market modeling. In fact its an area where prediction can be applied directly. Risk/Return optimization is the other important aspect in financial market modeling but that is secondary to prediction. The third important aspect is pricing especially of derivative products with non-linear payoffs. This third part is more or less solved and there are standard practices here.
So it boils down to prediction and optimisation when it comes to modeling in markets. And this is a very fertile area. Prediction in financial markets especially is very interesting because of the null hypothesis of efficiency of markets. In other words all information is already priced in. This induces certain discipline in predictive modeling. Also if there is a model that is able to predict better than the market, eventually market forces will make the model less and less accurate. Several non-finance professionals be it analytics professionals, statisticians or machine learning professionals do not completely appreciate the gravity of this hypothesis. Thus they get carried away by initial success of a particular model that might have worked in a particular regime. This is where strong understanding of financial principals are needed for building a robust predictive model and the model itself requires continuous monitoring and updating. Thus there will never be a dearth of demand for quants in financial markets.
Optimisation on risk-reward is actually an easier part but still many people (even in finance) do not appreciate the power of this. Here particular finance knowledge will not add significant value except that having some understanding of CAPM and other models will make optimisation a much simpler task.
The last bit of pricing models is one are where quants have pored significant brain power and it has come to a stage where the models are stable and can be taken off the shelf. Now a days people no longer build new option pricing models instead they have moved the focus to better prediction of volatility. Which is a good thing cause the problem has moved from the world of complex stochastic calculus to a simpler world of statistical analysis. Nevertheless there is work to be done in building computationally efficient models. For example some problem statements are: how to parallelize a path-dependent simulation? how to reduce number of simulations required drastically? etc.. This is a place where core quants and computer scientists can come together.
To conclude there is a strong need for data analysts, quants and statisticians in financial markets but they need to add on some financial skills before jumping on to modeling.
