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.
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.
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Design, develop, validate, and maintain algorithms for sensor data analysis, partial-discharge monitoring, and predictive analytics.
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Translate complex business and engineering requirements into robust, production-ready algorithms.
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Analyse large-scale sensor, time-series, and operational datasets to identify meaningful patterns and improve monitoring platform performance
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Develop signal-processing, feature-engineering, statistical-modelling, and machine-learning pipelines.
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Collaborate with software and platform engineers to integrate algorithms into the cloud monitoring platform and edge environments.
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Monitor deployed algorithm performance and contribute to model maintenance, retraining, and continuous improvement.
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Join one of Europe’s most promising deep-tech scale-ups.
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Shape the future of a rapidly growing multidisciplinary R&D organization.
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Competitive salary and benefits package.
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Enjoy regular team activities and company events.
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Fresh team lunches provided 3 times/week.
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Strong professional experience with Python and commonly used scientific and data-science libraries (e.g., pandas, NumPy, SciPy, scikit-learn).
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Strong foundation in statistical analysis, algorithm development, machine learning, and data modelling.
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Practical experience analysing sensor signals and time-series data.
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Solid understanding of signal-processing techniques such as filtering, denoising, spectral analysis, Fourier or wavelet transforms, and dimensionality reduction.
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Experience developing, validating, and deploying machine-learning models in production or cloud environments.
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Ability to connect patterns in data with physical processes and real-world system behaviour.
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Experience with at least one deep-learning framework, such as PyTorch, TensorFlow, or JAX.
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Ability to write clear, modular, tested, and maintainable Python code.
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Experience contributing to end-to-end data-science or ML projects, from problem framing and experimentation to deployment and monitoring.
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You have at least 3 years of professional experience in data science with a focus on sensor data analysis.
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Familiarity with real-time systems, edge computing, or predictive maintenance solutions is a plus.
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M.Sc. or Ph.D. degree in a relevant discipline in science or engineering.
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Strong analytical and problem-solving skills.
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Solid understanding of mathematics and physics, with the ability to apply theory to real engineering problems.
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A pragmatic, hands-on mindset and willingness to work with imperfect real-world data.
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You have strong verbal and written communication skills in English (Dutch is an advantage).
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Ability to work independently, take ownership, and collaborate effectively across disciplines.
Nice to Have:
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Experience with partial-discharge, acoustic, vibration, electrical, or other condition-monitoring data.
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Experience with predictive maintenance, anomaly detection, fault diagnosis, or remaining-useful-life estimation.
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Knowledge of time-series and general-purpose databases such as QuestDB, MongoDB, or PostgreSQL.
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Experience with experiment tracking, dataset or model versioning, and reproducible ML workflows.
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Experience with GPU computing, CUDA, model optimization, or real-time edge deployment.
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Familiarity with MLOps practices, automated testing, CI/CD, and production model monitoring.
Security & Compliance:
You'll be working with clients in the defense and security sectors, obtaining a Certificate of No Objection issued by the AIVD (Dutch General Intelligence and Security Service) is mandatory. This means you will need to undergo a security screening. For more information, please refer to details on security screening.
