Technical Skills & Expertise

Bridging classic quant approaches with modern machine learning

• Quantitative modeling for financial applications
• Volatility surface modeling for Deep Hedging
• Reinforcement Learning & Optimal Control
• Gaussian Processes & probabilistic modeling
• Bayesian Deep Learning & Variational Inference
• Model-based Reinforcement Learning
• Risk management and pricing workflows

Building resilient ML systems at scale

• End-to-end production ML pipelines
• Model training & deployment at scale
• CI/CD for machine learning
• Docker containerization
• AWS cloud infrastructure
• Unit & integration testing
• MLOps best practices
• Privacy-preserving ML implementation

Languages, frameworks, and tools

• Python (primary)
• TensorFlow & PyTorch
• NumPy, SciPy, Pandas
• scikit-learn, GPflow
• C/C++ (past work)
• Verilog/VHDL (hardware background)
• Git version control
• Linux/Unix environments

Core research competencies

• Experimental design & execution
• Statistical analysis & hypothesis testing
• Technical writing & publication
• Peer review process
• Conference presentations
• Research collaboration
• Mentoring junior researchers

Application areas and industry knowledge

• Equity derivatives & Deep Hedging
• Energy markets & commodity trading
• Natural gas & LNG structured assets
• Financial risk management
• Synthetic data generation
• Privacy-preserving ML
• Signal processing & tracking (past)

Leadership and collaboration

• Technical leadership & team management
• Cross-functional collaboration
• Stakeholder communication
• Mentoring & knowledge transfer
• Project planning & execution
• Research to production transition
• Problem decomposition & solution design