Lead Data Scientist with PyTorch

We're looking for a Lead Data Scientist skilled in PyTorch to collaborate with a multidisciplinary team of Geoscientists, Geophysicists, Data Scientists, and Machine Learning Engineers. The role involves translating technical and business requirements into reliable, high-impact deep learning solutions for seismic data, while also enhancing existing seismic ML tools and uncovering new business applications. Responsibilities Build and apply deep learning models (U-Nets, CNNs, and related architectures) to denoise and structurally interpret 2D and 3D seismic data Enhance the accuracy, robustness, and performance of current seismic ML tools through retraining, architecture improvements, and hyperparameter optimization Create models for structural interpretation needs, including fault detection, horizon tracking, and seismic facies classification Collaborate with Geoscientists and Geophysicists to validate outputs against expert interpretations and well data, incorporating their input into model refinements Spot, assess, and recommend new business opportunities where computer vision and deep learning could benefit exploration and production workflows Keep pace with advances in computer vision and seismic ML research, evaluating new techniques for real-world application Maintain sound engineering standards for reproducible experiments, model versioning, and production deployment Requirements A minimum of 5 years in comparable roles (ML Engineer, Data Scientist, or Geophysicist with strong ML expertise), with practical experience applying deep learning to real-world image or volumetric datasets Solid grasp of Computer Vision concepts, such as image segmentation, denoising, feature extraction, and convolutional architectures Strong command of PyTorch for constructing, training, and assessing deep learning models Deep knowledge of U-Net, CNN, and similar architectures applied to 2D and 3D seismic datasets Experience within the Upstream Oil & Gas sector, collaborating with Geoscience teams and seismic datasets Familiarity with seismic data fundamentals, including acquisition and processing principles, seismic attributes, noise characteristics, and structural elements like faults and horizons Excellent Python programming abilities Nice to have Exposure to transformer-based or self-supervised learning models for vision or seismic applications Knowledge of MLOps platforms (such as MLflow), Docker, and cloud-based AI services (Azure, AWS, or GCP)