Data Analytics and AI Technical Lead

Data Analytics and AI Technical Lead

Make Power for Good

RES is the world's largest independent renewable energy company. Our mission is simple: a future where everyone has access to affordable, zero-carbon energy. The problems we're solving are among the most important of our generation — and the people working on them are extraordinary.

We're growing our data and AI capability and looking for someone who genuinely loves working with data, machine learning, and generative AI to solve hard business problems. If you want your analytical work to drive real decisions — within a company that's changing the world — this is the role.

You'll own the delivery of analytics and AI products at RES — turning complex data into trusted, governed insight consumed by senior leaders and decision-makers across the business.

This is a hands-on technical role with 10+ years of experience required. You'll build models, write Python and SQL, apply LLMs and machine learning to business questions, and deliver automation that cuts manual effort and unlocks new insight. You'll focus your energy on analysis, AI application, and insight delivery — working with modern cloud platforms and pre-built data infrastructure than building pipelines from scratch.

Everything you deliver will be accurate, explainable, and appropriately governed.

• Deliver advanced analytics across complex business domains — defining, validating, and owning the metrics and models that drive decisions.
• Apply machine learning to real business problems: predictive modelling, classification, segmentation, anomaly detection, and forecasting.
• Use LLMs and generative AI to build analytical tools, AI-assisted insight products, and intelligent automation — including prompt engineering and retrieval-augmented generation (RAG).
• Validate all AI and model outputs for accuracy, bias, and reliability before they reach stakeholders.
• Translate analytical findings into clear, business-ready insight for senior and executive audiences.

• Identify and deliver automation opportunities using tools such as Power Platform, Power Automate, or equivalent — reducing manual effort and improving data-driven workflows.
• Use Python and SQL to clean, model, and analyse data; deliver self-service analytics through governed semantic models and reporting layers.

• Apply data classification, access controls, and quality standards to all analytical outputs.
• Embed responsible AI principles — fairness, explainability, and appropriate use — into every solution you deliver.

• Lead UAT and business validation for analytics outputs and AI products.
• Work closely with business stakeholders to understand problems, shape analytical approaches, and land insight in a way that drives action.
• Support teams in moving from manual reporting to governed, AI-enabled analytics.

• Python and SQL — strong, hands-on proficiency for data analysis, modelling, and scripting.
• Machine learning — practical experience building and applying supervised and unsupervised models to real business problems.
• Generative AI and LLMs — prompt engineering, RAG, and applied use of LLM tooling in an analytical context.
• Automation tooling — experience with platforms such as Power Platform, Power Automate, or equivalent.
• Data visualisation — ability to design clear, accurate, executive-ready outputs using tools such as Power BI or equivalent.
• Responsible AI — experience applying fairness, explainability, and governance principles to analytical products.
• Stakeholder communication — confident translating complex technical findings into plain language and actionable recommendations.

• Degree in data science, data analytics, computer science, statistics, or a related field — or equivalent hands-on experience.
• 10+ years' experience as a technical analyst, delivering advanced analytics and AI solutions with measurable business impact.
• Strong Python and SQL skills applied in a real analytical context.
• Proven experience applying machine learning and/or generative AI tools to business problems.
• Solid understanding of data governance, quality,…