How many minima does the residual sum of squares have for the logistic curve?
Sigmoid itself is not a convex function (see this) and square loss based on sigmoid, such as $ \left( A - \frac{1}{1 + e^{-z}} \right)^2 $ is not convex.

Simply plotting the square loss function with A = 5 shows that it's convex for x > 0 and concave, otherwise



If you want mathematical proof, take the second derivative and you'll see that it not strictly positive nor negative.