Data Scientist

Description: What's the role The Deep Learning Data Scientist will be responsible for researching and developing deep learning technologies to address business challenges. This role offers the opportunity to drive AI innovation in close collaboration with business stakeholders, delivering tangible commercial value.

You will design, implement, and evaluate machine learning solutions across diverse datasets (e.g., upstream, renewables), and work alongside engineers to continuously enhance and evolve products throughout their lifecycle. Skills and Requirements Must have legal authorization to work in the US on a full-time basis for anyone other than current employer.

Demonstrated ability to translate business problems into effective technical solutions and execute their implementation. Strong understanding of deep learning, including CNN and Transformer. Proven technical expertise in developing advanced analytics algorithms and models.

Strong problem-solving mindset with a continuous learning attitude. Good awareness of data management practices and product lifecycle processes. Proven experience in prototyping and implementing AI solutions for domain-specific problems.

Demonstrated skills in people management and effective stakeholder engagement. Regularly following the scientific journals and publishing in scientific journals or industrial conferences.

Job Experience Requirements

Ph.D or Masters in STEM fields: computer science, geophysics, engineering, mathematics, theoretical science, statistics, and 2+ years' work experience in deep learning technologies.

Practical experience in graduate studies or via work experience in the following areas are preferred: Experience in the energy industry, with an understanding of typical technical challenges and industry-standard approaches Hands-on experience designing and implementing deep neural networks, with strong intuition for trade-offs across model architectures and components Solid knowledge of state-of-the-art Multimodal Large Language Model (LLM) architectures and algorithms Familiarity with agentic workflows and their practical applications (e.g., AI foundry) Experience using Git for source code management Strong proficiency in PyTorch Experience with distributed model training on high-performance computing (HPC) clusters, either on cloud platforms or on-premises Experience with cloud platforms and architectures (e.g., Azure, AWS), along with comfort working in Unix-based environments (e.g., Bash)| Data Analysis, Data Engineering, Data Science