Senior Data Scientist
Job Description
Job Summary
Serves as a senior technical contributor within Data Science organization, providing strong expertise in scientific machine learning and advanced analytics for complex subsurface problems. The role combines hands-on model development with technical leadership across major initiatives, supporting the development of reusable learning systems for subsurface data, including foundation-model–style representation learning. The position emphasizes scientific rigor, technical influence, and cross-team collaboration, contributing to the design and evolution of large-scale learning systems while working alongside other senior technical leaders.
Responsibilities
- Lead the design, implementation, and evaluation of scientific machine learning models for subsurface and energy-related data.
- Contribute to the development of large-scale representation learning systems, including self-supervised and weakly supervised approaches.
- Provide technical guidance and review for complex modeling initiatives, ensuring robustness, generalization, and reproducibility.
- Own major technical workstreams and deliver scalable analytical solutions from research through deployment.
- Collaborate closely with senior data scientists, domain experts, and engineering teams to align technical solutions with business and scientific objectives.
- Guide experimentation practices, model evaluation standards, and technical documentation.
- Mentor data scientists and support knowledge sharing across the organization.
- Participate in external research activities, publications, or technical collaborations.
Key Competencies
- Scientific Machine Learning Expertise: Strong understanding of ML applied to physical or scientific systems.
- Large-Scale Representation Learning: Experience with modern deep learning architectures and training workflows for complex datasets.
- Technical Leadership: Ability to guide technical workstreams and influence outcomes through expertise.
- Experimental Rigor: Strong focus on hypothesis-driven development and reproducible experimentation.
- Collaborative Influence: Works effectively within multi-lead, interdisciplinary environments.
- Mentorship: Supports development of technical talent and best practices.
Qualifications
- MSc or PhD in Machine Learning, Data Science, Applied Mathematics, Physics, Geophysics, or a related technical discipline.
- Typically 5–10 years of experience in applied data science or research-oriented machine learning roles.
- Strong background in modern deep learning and scientific ML applied to complex or large-scale datasets.
- Experience leading technical initiatives or complex modeling projects.
- Experience in energy, geoscience, or large-scale scientific/industrial domains preferred
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