About Wood Mackenzie
Wood Mackenzie is the global data and analytics business for the renewables energy and natural resources industries. Enhanced by technology. Enriched by human an ever-changing world companies and governments need reliable and actionable insight to lead the transition to a sustainable future. Thats why we cover the entire supply chain with unparalleled breadth and depth backed by over 50 years experience. Our team of over 2400 experts operating across 30 global locations are enabling customers decisions through real-time analytics consultancy events and thought leadership. Together we deliver the insight they need to separate risk from opportunity and make confident decisions when it matters most.
Wood Mackenzie Brand Video
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
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 team s 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 to accommodate different time zones.
Equal Opportunities
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
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.
Key Skills
- Industrial Maintenance
- Machining
- Mechanical Knowledge
- CNC
- Precision Measuring Instruments
- Schematics
- Maintenance
- Hydraulics
- Plastics Injection Molding
- Programmable Logic Controllers
- Manufacturing
- Troubleshooting
Employment Type : Full-Time
Experience : years
Vacancy : 1
