Senior Data Scientist in London

Wood Mackenzie is the global leader in analytics, insights and proprietary data across the entire energy and natural resources landscape. For over 50 years our work has guided the decisions of the world's most influential energy producers, utilities companies, financial institutions and governments. Now, with the world's energy system more complex and interconnected than ever before, sector-specific views are no longer enough. That's why we've redefined what's possible with Intelligence Connected.

By fusing our unparalleled proprietary data with the sharpest analytical minds, all supercharged by Synoptic AI, we deliver a clear, interconnected view of the entire value chain. Our trusted team of 2,700 experts across 30 countries breaks siloes and connects industries, markets and regions across the globe. This empowers our customers to identify risk sooner, spot opportunities faster and recalibrate strategy with confidence – whether planning days, weeks, months or decades ahead.

The Senior Data Scientist will play a pivotal leadership role in building AI-native capabilities for both Synoptic, Wood Mackenzie's AI-first innovation unit, and the broader Energy & Natural Resources consulting portfolio. This role will be a core contributor in the development of cross-domain AI systems, knowledge-graph–powered analytics, and advanced forecasting models that support high-impact commercial workflows such as portfolio scenario analysis, M&A intelligence, forecasting, and energy transition planning.

Main responsibilities

• Working in the central machine learning department, you will be collaborating with our product, data, research, modelling, data science and engineering teams and reporting to the head of Applied AI.
• You will drive revenue growth by expanding our capabilities, assets and end-to-end AI solutions.
• Build machine learning and forecasting models supporting cross-commodity scenario analysis, energy transition planning, and strategic investment decision making.
• Work closely with embedded SMEs to encode domain knowledge into machine-readable structures that enable causal reasoning across global energy systems.
• Conduct exploratory analysis across large-scale, high-dimensional datasets spanning commodities, assets, infrastructure, and markets.
• Collaborate with engineers to design and implement scalable data pipelines and model deployment workflows.
• Support consulting engagements by developing analytical models, running simulations, and generating insight-rich deliverables.
• Work with product and research teams to validate models with early users and iteratively improve model performance.
• Document modelling approaches, contribute to code quality and standards, and participate in internal technical reviews.

We are a hybrid working company and the successful applicant will be expected to be present in the office at least two 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.

Key Skills & Experience

• You will be passionate about solving complex customer problems and bringing great products to market.

Essential Skills

• Strong experience applying machine learning or statistical modelling to real-world datasets.
• Strong experience with Python and ML libraries (e.g., scikit-learn, PyTorch, XGBoost).
• Experience working with complex, multi-domain or high-dimensional datasets.
• Demonstrated ability to work in cross-functional teams with engineers, analysts, and domain experts.
• Strong analytical thinking and problem-solving skills.

Preferred Skills

• Understanding of energy markets, asset modelling, or related analytical domains.
• Experience in consulting or client-facing analysis is advantageous.

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.