Internship - Data Science and Analytics

Organizational Context

The Department of Safeguards serves as the IAEA's global nuclear inspectorate, dedicated to preventing the spread of nuclear weapons. It develops and implements safeguards to ensure nuclear material is used for peaceful purposes and that no undeclared activities exist. This role is within the Safeguards Division of Information Management.

Job Purpose

This internship focuses on data science and analytics within the Safeguards Division of Information Management. The intern will apply analytical and machine learning skills, with a particular interest in large language models, multimodal models, and generative AI, to develop cutting-edge data science solutions. The role offers the opportunity to work alongside experienced data scientists, supporting analysts and subject matter experts in collecting and validating safeguards-relevant information. This involves training and evaluating models across various data types, ensuring model explainability and robustness, and exploring advancements in Artificial Intelligence to enhance safeguards processes.

Responsibilities

As an intern in the SGIM Innovation Team, the incumbent will enhance analytical capabilities and receive professional development. Key responsibilities include building rapid prototypes and proofs of concept to assess fit and usability. The intern will continuously improve machine learning models for tasks such as text and image analysis, classification, similarity search, and named entity recognition. Collaboration with business users to define scope and business cases is essential. The role involves developing and applying machine learning and deep learning algorithms for advanced analytics product functionality, and identifying valuable data sources to improve collection, processing, and analysis efficiency.

Work Experience

While specific work experience is not detailed, the role requires a strong academic background in data science, computer science, applied mathematics, statistics, or computational science, coupled with demonstrated skills in Python and an interest in machine learning.

Skills

Proficiency in Python, experience with machine learning methodologies (supervised/unsupervised learning, deep learning), understanding of large language models and multimodal models, knowledge of ML libraries (PyTorch, Transformers, Hugging Face), strong analytical skills, and excellent written and spoken English are essential.

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