Salary: £40,000 - 70,000 per year Requirements: We require 3 years of data science experience in a relevant industry. We require demonstrated experience with Python and cloud services, preferably AWS and/or Databricks. We require working knowledge of S3, PostgreSQL, and database design principles. We require a track record of delivering end-to-end AI and analytics systems that generate measurable business value. We require a solid grasp of data science best practices, including reproducible pipelines, experiment tracking, version control, and CI/CD for ML models. We require experience thriving in a fast-paced analytics development environment and handling ambiguous problems with an abstract, solution-oriented mindset. We prefer experience working with high-frequency sensor data, SCADA data, reliability engineering, or condition monitoring for industrial assets. We prefer familiarity with machine learning, signal processing, probabilistic modelling, and generative AI techniques such as LLMs, RAG pipelines, and agentic workflows. Responsibilities: We collaborate with data scientists, data engineers, software developers, and domain experts to design, implement, and validate AI-powered methods and intelligent software solutions. We develop and optimise AI-driven algorithms that detect, diagnose, and prognose wind-turbine failure modes from high-frequency sensor data across multiple data sources. We create AI-augmented analytical methods that improve workflow efficiency, automation, and service quality. We build AI-enhanced probabilistic models to estimate remaining useful life and component failure probabilities. We design intelligent dashboards and automated alerts that translate model outputs into clear, actionable recommendations for engineers, operators, asset managers, and field technicians. We create and iterate on supervised, unsupervised, and other AI models to identify degradation patterns and predict failure trajectories. We leverage generative AI techniques, including LLMs, RAG pipelines, and agentic workflows, to build intelligent knowledge systems that support wind farm operator decision-making. We work closely with engineering teams and customers to co-create and test new solutions that demonstrate tangible value for end users. We establish AI model governance and responsible AI processes, including cross-validation, out-of-sample testing, and bias audits. We work with data engineers to deploy models to production via robust MLOps pipelines with automated monitoring for model drift and performance. We run field trials on live turbine fleets and support validation in production environments. We contribute to building AI-enabled software components and tooling. Technologies: AI AWS CI/CD Cloud Databricks Support Machine Learning PostgreSQL Python More: We are hiring a Data Scientist: AI & Advanced Analytics on a contract basis, outside IR35, to help us advance condition monitoring and predictive maintenance for wind-turbine assets. Our team works with an industry-leading historic dataset and a cross-functional group of data scientists, data engineers, software developers, and domain experts to deliver AI-powered solutions for wind turbine owners and operators. The role focuses on high-frequency vibration data, SCADA data, recorded failures, and emerging generative AI methods to create actionable insights, improve maintenance decisions, and support end users with intelligent tools, dashboards, and automated alerts. last updated 20 week of 2026