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.
Key Responsibilities:
- 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.
Why Join Us?
- 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.
Requirements:
- 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.
Qualifications:
- 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.
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.