Lead Data Scientist - Energy Domain

About the role

Capco is seeking a Lead Data Scientist to drive the development of core analytical models within the energy sector. This is a hands-on technical leadership role, combining deep expertise in machine learning and time series modelling with the ability to lead teams, shape technical direction and deliver complex data science initiatives from concept through to production. You will be responsible for leading the design, development and optimisation of advanced statistical and machine learning models that support energy planning, forecasting and strategic decision-making. Alongside technical delivery, you will manage stakeholder relationships, guide cross-functional teams across data, engineering and business domains, mentor junior data scientists, and ensure projects are delivered to a high standard using modern software engineering and MLOps practices. This role is ideal for someone who enjoys balancing hands-on model development with technical leadership, project delivery and helping others grow.

Responsibilities

• Utilise Python for advanced data analysis, exploration, feature engineering and data processing to uncover insights, trends and patterns.
• Develop and implement statistical models, machine learning models and algorithms, with a particular focus on time series modelling and forecasting.
• Support core model development, ensuring models are robust, scalable, explainable and aligned to business requirements.
• Work with large, complex datasets to build and maintain analytical models that support planning, reporting and decision-making.
• Collaborate closely with stakeholders and cross-functional teams to understand business needs, manage expectations and translate requirements into practical, data-driven solutions.
• Contribute to team ways of working through mentoring, knowledge sharing, code reviews and high-quality engineering practices.
• Apply Git-based version control and collaborative development practices to support robust, maintainable and auditable model development.
• Use Azure ML to support model development, deployment, monitoring and lifecycle management.
• Manage and optimise MLOps processes within the Azure environment, supporting efficient deployment, maintenance and performance tracking of models.
• Utilise Databricks for scalable data processing, analytics and model development.
• Ensure data quality, integrity and reliability through effective validation, governance and documentation.
• Stay up to date with the latest developments in data science, machine learning, cloud technologies and energy analytics to ensure best practices are applied.

Requirements

• Strong hands-on experience in Python for data science, analytics, modelling and machine learning.
• Deep experience in time series modelling, forecasting, statistical modelling and working with sequential or temporal datasets is deemed mandatory.
• Proven ability to develop and implement machine learning models and analytical algorithms in a production or enterprise environment.
• Experience working with large-scale datasets, data pipelines and analytical models.
• Strong understanding of model validation, performance evaluation, feature engineering and model explainability.
• Strong stakeholder management skills, with the ability to engage technical and non-technical audiences, manage expectations and communicate complex analytical outputs clearly.
• Proven ability to collaborate across teams, including data science, engineering, architecture, business and delivery teams.
• Experience mentoring team members, supporting knowledge transfer and contributing to a collaborative, high-performing team culture.
• Experience conducting code reviews and promoting high standards of code quality, maintainability and reproducibility.
• Practical experience using Git for version control in collaborative development environments.
• Experience supporting or contributing to MLOps processes, including model deployment, monitoring, maintenance and optimisation.
• Strong problem-solving skills and the ability to work in complex, ambiguous environments.
• Excellent communication skills, with the ability to explain technical concepts to both technical and non-technical stakeholders.
• Must-Have Experience
• Advanced Python programming for data science, data analysis and machine learning.
• Strong time series modelling and forecasting experience.
• Experience developing statistical, machine learning or optimisation models using real-world datasets.
• Strong stakeholder management and cross-team collaboration experience.
• Experience mentoring team members and contributing to effective team delivery.
• Experience with code review practices and Git-based version control.
• Ability to work with complex data structures and large-scale data environments.
• Strong understanding of data quali

Additional Information

Lead Data Scientist - Energy Domain Location: London (Hybrid) | Practice Area: Data & Analytics | Type: Permanent Build the models powering the future of energy system planning.

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