Let $x\in\mathbb{R}^{d}$ and $W\in \mathbb{R}^{d\times d}$ so $M(x)=I-2\frac{x^TW^Tx}{\|Wx\|^2}W\in\mathbb{R}^{d\times d}$.
I'd like to show $M(x)$ is invertible for all $x$ under as few constraints on $W$ as possible, e.g., it would be interesting if $M(x)$ is invertible for triangular, symmetric or positive/negative definite $W$.
Observations.
- Scale invariance: $M(c\cdot x)=M(x)$ for all $c\neq 0$.
- $x^TW^Tx/\|Wx\|^2$ resembles the generalized rayleigh quotient.
- Eigenvalues are $\lambda_i(M(x))=1-2\frac{x^TW^Tx}{\|Wx\|^2}\lambda_i(W)$. If $\lambda_i(M(x))\neq 0$ then $M(x)$ is invertible. This happens when the eigenvalues of $W$ satisfies $\lambda_i(W)\neq 1/2 \frac{\|Wx\|^2}{x^TWx}$.
- Skew symmetric $W^T=-W$ implies $M(x)=I$ which is not interesting.
- Diagonal $cI$ is implies $M(x)=-I$ which is not interesting.
To expand on my comment (this is still only a partial answer):
Take $d=2$ and $W$ diagonalisable with eigenvalues $\lambda_{1,2}$. Clearly, we can take $W$ in diagonal form (i.e. express $M$ in a basis where $W$ is diagonal).
If both eigenvalues are zero (i.e. $W\equiv0$), the expression is ill-defined anyway (already is if $x$ is in the kernel of $W$).
So assume that $\lambda_1\neq0$. Choose $$x=\begin{pmatrix}\sqrt{\frac{\lambda_2^2-2\lambda_1\lambda_2}{\lambda_1^2}}\\1\end{pmatrix}\,.$$ Then $M(x)$ is not invertible, as you can check by direct calculation.
Note that this includes my comment: If $\lambda_2=2\lambda_1$, $x_1=0$.
Of course, you can rescale $x$ by any constant factor.
Thus, there always exists an $x$ for which $M(x)$ is not invertible. This carries over to any matrix whose Jordan form contains $W$.
It's too late for me to check the case for only nontrivial Jordan blocks.
The bigger remaining question: The eigenvalues may not be real, and even if they are, the vector $x$ may not be. So your (only?) chance of finding an $M$ that is invertible for all $x$ is to lok for some constraints in this direction.