Data Scientist

Key Responsibilities

• Meet colleagues across practice areas to understand their work, identify specific use cases and agree what a useful result would look like.
• Scope and demonstrate focused proofs of concept, choosing suitable methods from statistical analysis and forecasting to machine learning and language models.
• Explain methods, limitations and potential value clearly to specialists and nontechnical colleagues.
• Use feedback to improve the solution and build confidence in the team’s services.
• Assess data quality, feasibility, privacy and delivery needs, and define the evidence needed to take a prototype further.
• Design and deliver data science and AI solutions across projects with the wider team, from modelling and evaluation to integration, documentation and adoption.
• Contribute to proposals and client presentations.
• Establish reusable ways of working and help turn early demonstrations into a sustainable pipeline of well-defined client work.

Requirements & Tech Stack

• Hands-on experience in Python for analysis and modelling, including common data and machine learning libraries.
• Ability to use SQL to extract, join and check data.
• Sound grounding in statistics and machine learning, including regression or classification, forecasting, validation, baseline comparisons and appropriate performance measures.
• Practical familiarity with using large language models in analysis or prototyping, including prompting, checking outputs against evidence and recognising privacy, accuracy and reliability risks.
• Experience preparing imperfect data, building reproducible analyses and writing maintainable code with version control and testing.
• Ability to take an ambiguous question from discovery to a working demonstration, explain assumptions and uncertainty, and judge whether a solution is worth developing for operational use.
• Confidence working independently and engaging stakeholders across teams.
• Clear written and spoken English for technical and nontechnical audiences.

Qualifications

• A relevant degree or equivalent practical experience in a quantitative field.

Useful Additional Experience

• Experience in energy, utilities, climate or environmental policy, particularly applied analysis with domain experts.
• Experience in consulting, public policy research or client-facing delivery.
• Familiarity with cloud platforms, APIs, data pipelines or deployment.
• Experience building AI agents or applications that connect language models to data and tools, such as retrieval-based systems.
• Note: The company can train someone with strong core skills.