Ph.D. in Operations Research / Big Tech Eng: How to transition into intermediate/advanced ML for high-value industries (Robotics, Defense, Finance)? [D]

I hold a Ph.D. in Operations Research, along with a BSc/MSc in Engineering and OR. I previously worked in Big Tech, but I’m currently looking to transition.

My primary goal is to upgrade my technical skillset to maximize my industry-related profitability and marketability. I want to get away from generic data science and move into high-value, math-heavy engineering and modeling roles.

• My Core Interests: Forecasting, predictive analytics, and machine learning applied to industrial settings.
• Target Industries: Robotics/Autonomous Systems, Defense/Aerospace, and Quantitative Finance.
• What I want to skip: I have little interest in doing core NLP/LLM research, though I am interested in RL, Multi-Agent systems, and applied AI.

Where I am right now: I have a solid grasp of optimization and basic/intermediate ML/stats. However, I want to bridge the gap into more intermediate/advanced ML topics that are actually useful and highly valued by employers. I want to get back into heavy math, but only if it drives real-world business value.

What I'm looking to learn:

• Causal Inference: (e.g., Structural Causal Models, Uplift modeling, Double ML).
• Tree-Based Math: Understanding things like XGBoost from the ground up (deriving gradients/hessians for custom loss functions, implementing from scratch).
• Reinforcement Learning / Control: Bridging the gap between OR dynamic programming and deep RL for robotics/defense.

My questions for the community:

1. Skill Prioritization: From a purely market-driven, high-compensation perspective, which specific ML topics should a Ph.D. in OR focus on to stand out in Robotics, Defense, or Banking/Finance?
2. Portfolio/Proof: How can I best demonstrate to employers that I have the engineering chops to implement these advanced models from scratch, rather than just calling APIs?
3. Positioning: How do I best market the "Predict-then-Optimize" sweet spot (combining ML predictions with OR optimization frameworks) to companies in these sectors?

Would love any advice on textbooks, specific frameworks to master, or strategies on how to position my background for maximum leverage. Thanks!