How does a Master's in Mathematics/Statistics sound for my case?

Context:

I have a Bachelor's in Electrical Engineering and a Master's in Computer Science. As you would expect, my Bachelor's in EE had a strong high-level mathematics component to it, but that was 3-4 years ago. My Master's in CS had little/no math. Right now I'm about to join a Bulge Bracket Investment Bank with Rates Electronic Trading team. I'm not sure exactly what sort of development I'll be doing and if it's directly related to the trading platform.

Case:

I'm looking to pivot myself toward a quantitative developer role in the near future. Specifically with hedge funds/buy-side firms like Citadel/Two Sigma, Jump Trading etc. Note that I'm not considering quantitative researcher or quantitative trader roles, just purely quantitative developer as I enjoy and want to work with software primarily but in the financial space. Now I'm very well aware that QDs will have to have a decent/strong mathematics background to be able to understand and communicate effectively with QRs and QTs and implement their solutions.

So given my background, do you think a second Master's in Mathematics/Statistics would solidify me for that path or is it just a complete waste of time/money? I could do the Master's in 1 year full-time or 2 years part-time. Or should I take online courses/refreshers?

Pure Mathematics electives:

• Algebra 3
• Algebra 4
• Algebraic Curves
• Algebraic Geometry
• Algebraic Number Theory
• Algebraic Topology
• Bifurcation Theory
• Classical Dynamics
• Commutative Algebra
• Complex Manifolds
• Differential Topology
• Dynamical Systems
• Dynamics of Learning and Iterated Games
• Elliptic Curves
• Formalising Mathematics
• Fourier Analysis and Theory of Distributions
• Functional Analysis
• Galois Theory
• Geometric Mechanics
• Geometry of Curves and Surfaces
• Graph Theory
• Group Representation Theory
• Group Theory
• Lie Algebras
• Manifolds
• Markov Processes
• Number Theory
• Probability Theory
• Random Dynamical Systems and Ergodic Theory: Seminar Course
• Riemannian Geometry
• Stochastic Calculus with Applications to non-Linear Filtering

Statistics:

• Probability for Statistics
• Fundamentals of Statistical Inference
• Applied Statistics
• Computational Statistics
• Statistics Research Project
• Introduction to Statistical Finance
• Advanced Statistical Finance
• Stochastic Processes
• Contemporary Statistical Theory
• Bayesian Methods
• Multivariate Analysis
• Machine Learning
• Biomedical Statistics
• Statistical Genetics and Bioinformatics
• Big Data
• Advanced Simulation Methods
• Data Science
• Deep Learning with TensorFlow
• Nonparametric Statistics
• Time Series Analysis
• Survival Models