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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 applied mathematics component to it, but that was 3-4 years ago. My Master's in CS had little/no math.

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 - i.e. Quantitative Software engineers who implement models in code from QResearchers/Traders in an efficient and optimized manner. 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?

Applied Mathematics electives:

• Applied Complex Analysis
• Asymptotic Methods
• Bifurcation Theory
• Classical Dynamics
• Computational Linear Algebra
• Computational Partial Differential Equations
• Dynamical Systems
• Dynamics of Games
• Finite Elements: Numerical Analysis and Implementation
• Fluid Dynamics I
• Fluid Dynamics II
• Function Spaces and Applications
• Introduction to Partial Differential Equations
• Markov Processes
• Mathematical Biology
• Mathematical Finance
• Methods for Data Science
• Numerical Solution of Ordinary Differential Equations
• Quantum Mechanics I
• Quantum Mechanics II
• Random dynamical systems and Ergodic Theory (Seminar Course)
• Scientific Computation
• Special Relativity and Electromagnetism
• Stochastic Differential Equations
• Tensor Calculus and General Relativity
• Vortex Dynamics

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