Partial Discharge Data Scientist

We are looking for an experienced Data Scientist to develop advanced analytics for our partial discharge monitoring and predictive maintenance platform.

Optics11 is a deep-tech company developing advanced fiber-optic sensing technologies for challenging environments and applications in energy and underwater security. Our systems enable high-performance sensing and monitoring through advanced photonics, signal processing, and data-driven intelligence.

We are expanding our partial discharge R&D team with a Data Scientist who is motivated to translate complex business and engineering challenges into scalable, data-driven solutions that enabling next-generation acoustic emission monitoring solutions by processing sensor signals to early detect, localize and quantify partial discharge in energy infrastructure.

You will work closely with cross-functional teams (embedded, software, and hardware) to design, develop, and deploy advanced algorithms that predict energy infrastructure failures., * Design, develop, validate, and maintain algorithms for sensor data analysis, partial-discharge monitoring, and predictive analytics.

• Translate complex business and engineering requirements into robust, production-ready algorithms.
• Analyse large-scale sensor, time-series, and operational datasets to identify meaningful patterns and improve monitoring platform performance
• Develop signal-processing, feature-engineering, statistical-modelling, and machine-learning pipelines.
• Collaborate with software and platform engineers to integrate algorithms into the cloud monitoring platform and edge environments.
• Monitor deployed algorithm performance and contribute to model maintenance, retraining, and continuous improvement.

Previous experience in partial discharge, acoustic emission or electrical signal processing is an advantage, but not a dealbreaker. We value strong analytical thinking, curiosity, and the willingness to learn new domains., * Strong professional experience with Python and commonly used scientific and data-science libraries (e.g., pandas, NumPy, SciPy, scikit-learn).

• Strong foundation in statistical analysis, algorithm development, machine learning, and data modelling.
• Practical experience analysing sensor signals and time-series data.
• Solid understanding of signal-processing techniques such as filtering, denoising, spectral analysis, Fourier or wavelet transforms, and dimensionality reduction.
• Experience developing, validating, and deploying machine-learning models in production or cloud environments.
• Ability to connect patterns in data with physical processes and real-world system behaviour.
• Experience with at least one deep-learning framework, such as PyTorch, TensorFlow, or JAX.
• Ability to write clear, modular, tested, and maintainable Python code.
• Experience contributing to end-to-end data-science or ML projects, from problem framing and experimentation to deployment and monitoring., * You have at least 3 years of professional experience in data science with a focus on sensor data analysis.
• Familiarity with real-time systems, edge computing, or predictive maintenance solutions is a plus.
• M.Sc. or Ph.D. degree in a relevant discipline in science or engineering.
• Strong analytical and problem-solving skills.
• Solid understanding of mathematics and physics, with the ability to apply theory to real engineering problems.
• A pragmatic, hands-on mindset and willingness to work with imperfect real-world data.
• You have strong verbal and written communication skills in English (Dutch is an advantage).
• Ability to work independently, take ownership, and collaborate effectively across disciplines.

Nice to Have:

• Experience with partial-discharge, acoustic, vibration, electrical, or other condition-monitoring data.
• Experience with predictive maintenance, anomaly detection, fault diagnosis, or remaining-useful-life estimation.
• Knowledge of time-series and general-purpose databases such as QuestDB, MongoDB, or PostgreSQL.
• Experience with experiment tracking, dataset or model versioning, and reproducible ML workflows.
• Experience with GPU computing, CUDA, model optimization, or real-time edge deployment.
• Familiarity with MLOps practices, automated testing, CI/CD, and production model monitoring.

• Join one of Europe’s most promising deep-tech scale-ups.
• Shape the future of a rapidly growing multidisciplinary R&D organization.
• Competitive salary and benefits package.
• Enjoy regular team activities and company events.
• Fresh team lunches provided 3 times/week.