Senior Data Scientist
Skills
About This Role
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 Computer Vision projects (classification, detection, segmentation) 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 Geoscience applications to the Oil & Gas sector is a plus.
Requirements – Key Skills
- Strong foundation in applied mathematics and statistics.
- Proficiency in machine learning and deep learning techniques.
- Advanced Python programming skills for AI development.
- Extensive experience with classic CV tools (OpenCV), deep learning frameworks (PyTorch, TensorFlow), and popular ML libraries (Scikit-learn).
- Comprehensive knowledge and practical application of diverse ML algorithms and DL architectures.
- Proficiency in essential development tools like PyCharm, Jupyter, ClearML, Git, and Docker.
- 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.
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