VP Machine Learning bei Woodmac

The Vice President of Machine Learning Systems is the senior technical executive responsible for the full lifecycle of machine learning within the Power and Commodities Trading and Medium-Term Solutions — from exploratory research to real-time production deployment. This leader ensures that modeling, engineering, and operational functions deliver cutting-edge, low-latency ML capabilities that directly support our clients’ workflows. 

The VP will be responsible for Modeling & Research, Machine Learning Systems Engineering, and MLOps  teams while also remaining hands-on — working side-by-side with data scientists, ML engineers, and MLOps specialists to solve complex challenges. This includes guiding model development for very short time horizons (hours to days) as well as medium-term predictive horizons (months to a year) to support both intraday trading and strategic decision-making. 

Key Responsibilities 

Strategic Leadership 

• Define and own the ML vision and architecture for the trading and mid-term solutions unit. 

• Balance innovation with performance, ensuring accuracy, latency, and reliability targets are met for all models. 

• Establish a technical strategy that serves both short-term and long-term trading horizons. 

Hands-On Technical Engagement 

• Actively participate in model architecture design, feature engineering, and prototype review. 

• Collaborate directly with ML engineers to optimize models for latency, memory efficiency, and throughput. 

• Work closely with the Modeling, Research, & Product teams to select appropriate techniques for different forecast horizons — from rapid market reaction models to medium-term price and congestion forecasts. 

Organizational Oversight 

• Lead and mentor the ML Corporate team ensuring alignment across modeling, systems engineering, and MLOps. 

• Drive cross-team communication to prevent bottlenecks and accelerate model delivery. 

• Foster a culture of collaboration, rigorous experimentation, and rapid iteration. 

Client Engagement 

• Serve as the senior technical point of contact for client traders, analysts, data scientists, and technology leads. 

• Translate complex ML capabilities into actionable solutions that improve trading execution and strategy, and implement native back testing functionality 

• Ensure that modeling priorities align with evolving client market views and needs. 

Execution & Delivery 

• Oversee end-to-end ML delivery: research → optimization → deployment → monitoring. 

• Ensure ML production systems meet low latency and high availability SLAs. 

• Implement governance for model versioning, retraining triggers, and model retirement. 

Required Experience 

• 10+ years in machine learning and quantitative systems with 3+ years in senior leadership, working with tree-based models (XGBoost, LightGBM), neural network models (LSTM, GNNs, CNNs, TFTs) and hybrid models (PINNs). 

• Proven track record deploying low-latency ML systems for trading, finance, or other high-performance applications. 

• Deep expertise in: 

• Time-series forecasting for both very-short-term and medium-term horizons. 

• Real-time model serving optimization (quantization, pruning, distillation). 

• MLOps platforms, high-throughput data pipelines, and distributed inference systems.  

• Comfortable working hands-on with Python ML stacks, and experience working with GPU acceleration frameworks, and real-time data environments. 

• Experience collaborating directly with stakeholders to meet performance-critical objectives. 

• Advanced degree (MS/PhD) in Computer Science, Applied Mathematics, Electrical Engineering, or related field preferred. 

Reporting Structure 

Reports to: Chief Product and Technology Officer 

Expectations 

• We are a hybrid working company and the successful applicant will be expected to be physically present in the office at least 2 days per week to foster and contribute to a collaborative environment, but this may be subject to change in the future.  

• Due to the global nature of the team, a degree of flexible working will be required to accommodate different time zones.  

Wood Mackenzie Values 

• Inclusive – we succeed together 

• Trusting – we choose to trust each other 

• Customer committed – we put customers at the heart of our decisions 

• Future Focused – we accelerate change 

• Curious – we turn knowledge into action