We are looking for a Senior Data Scientist to join our journey to reinvent how power‑grid capacity is unlocked. The ideal candidate combines deep expertise in AI, physics, and engineering, with a strong track record in handling raw sensor and time-series data. They should excel in signal processing, statistical modeling, and building production-grade analytics and data pipelines. The role demands both software engineering skill and rigorous, safety-conscious development, as it impacts critical infrastructure.
What Youll Do:
Low‑Level Signal Processing Transform raw millisecond‑scale waveforms into meaningful features: design signal processing pipelines, extract spectral and temporal signatures of wind‑induced motion, and craft features that power the next layers of modelling.
Research, train, and optimize models that infer local wind speed and direction, conductor temperature and strain, and detect anomalous eventsleveraging cutting‑edge AI techniques to explore a fascinating, largely untapped domain.
Deliver physical and mathematical insights from the data; work closely with academic partners to design preprocessing, augmentation, and physics‑context layers that translate wind‑induced vibrations into accurate wind metrics.
Write, test, and maintain reliable code that operates 24/7 in production and integrates seamlessly with utility systems.
Shape the teams data roadmap, mentor peers, and champion best practices in MLOps, experimentation, and documentation.
Requirements: Advanced degree (M.Sc. or Ph.D.) in Electrical Engineering, Physics, Applied Mathematics, Computer Science, or a related quantitative discipline.
5+ years of hands-on experience in developing and deploying machine learning, signal processing, or algorithmic solutions, with emphasis on raw or low-level data (e.g., sensor data, audio, video streams, or medical imaging).
Proven expertise in time-series analysis and handling large-scale, complex datasets from acquisition to production deployment.
Strong Python programming skills, with the ability to write clean, modular, and testable production-grade code.
Demonstrated experience deploying ML/DSP pipelines into production environments, ideally in high-availability systems.
Familiarity with ML/DL frameworks such as PyTorch, TensorFlow, scikit-learn, and gradient-boosting libraries (e.g., XGBoost, LightGBM).
Strong collaboration and communication skills, with the ability to work effectively across interdisciplinary teams including software engineers, physicists, and external stakeholders.
This position is open to all candidates.