Senior Data Scientist

Job Description

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 & Gassector 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.