Senior Data Scientist

Overview:

-

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.

Qualifications:

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.