We are seeking an experienced Senior Data Scientist to join our team operating in the Oil and Gas sector, specifically in upstream optimization. As a Senior Data Scientist, you will play a crucial role in extracting insights from complex datasets, developing data-driven algorithms, and implementing AI solutions to optimize oil production operations. Your expertise in machine learning, deep learning, and optimization techniques will be vital in solving industry-specific challenges and driving efficiency gains in oil production processes. You will work closely with cross-functional teams to analyze data, implement, deploy and maintain solution, and deliver actionable recommendations to improve production management and maximize operational performance.
Responsibilities:
Experimentation and Prototype Development: Design, develop, and assess data-driven algorithms for various tasks (regression, classification, segmentation, etc.) using cutting-edge AI techniques.
Prototype and evaluate LLM-based systems and multimodal models for tasks such as document understanding, knowledge extraction, and workflow automation.
End-to-End Implementation: Hands-on implementation of AI models, from data preparation and cleaning to model deployment and maintenance in production.
Integration of Pipelines: Contribute to the solution design and collaborate with teams to integrate AI-enabled software products for the oil & gas industry.
Monitoring Model Performance: Evaluate and monitor AI solutions to ensure they align with project objectives, addressing data quality issues and continuously improving existing solutions.
Requirements Experience
At least 4 years of experience demonstrating depth and breadth in strong>C/strong> strong>omputer /strong> strong>Vi/strong> strong>sion /strong>projects (strong>classification, detection/strong> strong>, segmentation/strong>) with CV approaches.
Demonstrated experience with state-of-the-art machine-learning and/or deep-learning technologies.
Demonstrated experience in developing, deploying and scaling end to end ML pipelines in industrial context.
Hands-on experience building and deploying LLM applications (e.g., GPT, Llama, Falcon, Claude), including fine-tuning, RAG systems, domain adaptation, or multimodal extensions.
Demonstrated experience designing and implementing agentic AI systems task-oriented agents, workflow orchestrators, tool-using agents, or autonomous reasoning frameworks.
Experience in strong>Geoscience/strong> applications to the strong>Oil/strong> strong> & /strong> strong>Gas/strong> strong> /strong>sector is a plus.
Requirements /strong> strong> /strong> strong>Key Skills/strong> /p>
Strong foundation in applied mathematics and statistics.
Proficiency in machine learning and deep learning techniques.
Advanced strong>Python/strong> programming skills for AI development.
Extensive experience with classic CV tools (strong>OpenCV/strong>), deep learning frameworks (strong>PyTorch/strong> strong>, TensorFlow/strong>), and popular ML libraries (strong>Scikit-learn/strong>).
Comprehensive knowledge and practical application of diverse ML algorithms and DL architectures.
Proficiency in essential development tools like strong>PyCharm/strong>, strong>Jupyter/strong>, strong>ClearML/strong>, strong>Git/strong>, and strong>Docker/strong>.
Strong knowledge of LLM frameworks and tooling (LangChain, LlamaIndex, Hugging Face, OpenAI/Anthropic APIs).
Proficiency in prompt engineering, evaluation frameworks, and structured prompt design.
Familiarity with agent frameworks (LangGraph, AutoGen, CrewAI, or custom agent architectures).
Autonomy in problem-solving and project execution, demonstrating the ability to work independently and in a team framework.
Excellent communication skills for conveying technical concepts effectively.
Educational Requirements
Master s degree or Ph.D. in Computer Science, Applied Mathematics, Statistics, or any AI-related field.
