  The conditional logit model is a standard workhorse approach to estimating
customers' product feature preferences using choice data. Using these models at
scale, however, can result in numerical imprecision and optimization failure
due to a combination of large-valued covariates and the softmax probability
function. Standard machine learning approaches alleviate these concerns by
applying a normalization scheme to the matrix of covariates, scaling all values
to sit within some interval (such as the unit simplex). While this type of
normalization is innocuous when using models for prediction, it has the side
effect of perturbing the estimated coefficients, which are necessary for
researchers interested in inference. This paper shows that, for two common
classes of normalizers, designated scaling and centered scaling, the
data-generating non-scaled model parameters can be analytically recovered along
with their asymptotic distributions. The paper also shows the numerical
performance of the analytical results using an example of a scaling normalizer.
