VP Machine Learning

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Wood Mackenzie is the global data and analytics business for the renewables, energy, and natural resources industries. Enhanced by technology. Enriched by human intelligence. Our team of over 2,400 experts, operating across 30 global locations, enables customers’ decisions through real-time analytics, consultancy, events and thought leadership. We deliver the insight they need to separate risk from opportunity and make confident decisions when it matters most.

Wood Mackenzie values include: 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.

• 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
  • Participate in model architecture design, feature engineering, and prototype reviews.
  • Collaborate with ML engineers to optimize models for latency, memory efficiency, and throughput.
  • Work with the Modeling, Research, & Product teams to select 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.

• 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 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.

Reports to: Chief Product and Technology Officer

• 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.

We are an equal opportunities employer. This means we are committed to recruiting the best people regardless of their race, colour, religion, age, sex, national origin, disability or protected veteran status. You can find out more about your rights under the law at www.eeoc.gov. If you are applying for a role and have a physical or mental disability, we will support you with your application or through the hiring process.

We’re not providing personal details such as location except as stated above. Referrals increase your chances of interviewing at Wood Mackenzie by 2x. Get notified about new Vice President of Machine Learning jobs in Edinburgh, Scotland, United Kingdom.