Contract
Outside IR35
You will collaborate with data scientists, data engineers, software developers and domain experts to design, implement, and validate AI‑powered methods and intelligent software solutions for condition monitoring and predictive maintenance of wind‑turbine assets.
You will be working with an industry leading historic dataset to apply signal processing techniques, reliability engineering principles, machine learning models and emerging generative‑AI methods to extract actionable insights for wind turbine owners and operators. The role will include working with high‑frequency vibration data, SCADA data, and large collections of recorded failures in a cross-functional team.
Key ResponsibilitiesSignal Processing & Data Analysis
- Develop and optimise AI‑driven algorithms that detect, diagnose, and prognose wind‑turbine failure modes from high‑frequency sensor data across multiple data sources.
- Collaborate with domain experts to create new AI‑augmented analytical methods that improve workflow efficiency, automation, and service quality.
- Build AI‑enhanced probabilistic models to estimate remaining useful life (RUL) and component failure probabilities.
- Design intelligent dashboards and automated alerts that translate model outputs into clear, actionable recommendations for Condition Monitoring Engineers, operators, asset managers, and field technicians.
- Build AI enabled software bits and pieces…
- Create and iterate on supervised, unsupervised, and other AI models to identify degradation patterns and predict failure trajectories.
- Leverage generative‑AI techniques (including LLMs, RAG pipelines, and agentic workflows) to construct intelligent knowledge systems that support and accelerate wind farm operator decision‑
- Work closely with engineering teams and customers to co‑create and test new solutions, demonstrating tangible value for end users.
- Establish AI model‑governance and responsible‑AI processes, including rigorous validation (cross‑validation, out‑of‑sample testing, bias audits)
- Work with Data Engineers to deploy models to production via robust MLOps pipelines with automated monitoring for model drift and performance, including running field trials on live turbine fleets.
- 3 years of data‑science experience in a relevant industry.
- Demonstrated experience with Python and cloud services (preferably AWS and/or Databricks).
- Working knowledge of S3, PostgreSQL, and database design principles.
- Track record of delivering end‑to‑end AI and analytics systems that generate measurable business value.
- Solid grasp of data‑science best practices, including reproducible pipelines, experiment tracking, version control, and CI/CD for ML models.
- Experience thriving in a fast‑paced analytics development environment and handling ambiguous problems with an abstract, solution‑oriented mindset.
