Back to SearchLead Data Scientist with PyTorch
Remote in Belarus, & 4 others
Data Science& 4 others
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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)