Internship – Data Science and Analytics
International Atomic Energy Agency (IAEA) · Vienna, Austria
- On site
- Internship
- Vienna, Austria
Job highlights
- Gain practical data science experience at IAEA.
- Work with machine learning, LLMs, and generative AI.
- Support safeguards information analysis and validation.
- Develop prototypes and improve ML models.
- Internship offers professional development opportunities.
About the role
About the Internship Program
The IAEA accepts a limited number of interns each year. Internships are awarded to individuals pursuing or recently awarded a university degree. The program aims to provide interns with practical work experience aligned with their studies, expose them to the work of the IAEA and the UN, and benefit IAEA programs through the assistance of qualified students.
Internships typically last between three months and one year.
Organizational Setting
The Department of Safeguards is the IAEA's global nuclear inspectorate, working to prevent the spread of nuclear weapons. Its role involves developing and implementing safeguards to ensure no diversion of declared nuclear material and no undeclared nuclear material or activities exist. The Department includes inspectors for on-site verification and technical staff who develop safeguards concepts, equipment, provide analytical services, evaluate information, manage IT infrastructure, and support program coordination.
The Division of Information Management (SGIM) utilizes state-of-the-art methodologies for collecting, processing, and analyzing safeguards-relevant information. This includes state declarations, analysis of samples and measurements, and open-source data like nuclear trade information and satellite imagery. SGIM staff validate data and perform complex analysis to support field activities and state evaluations.
Main Purpose of the Role
The Safeguards Division of Information Management seeks a passionate Data Science and Analytics Intern with strong analytical skills and an interest in machine learning, particularly large language models, multimodal models, and generative AI. This role offers the opportunity to contribute to cutting-edge data science solutions within an international organization.
As an intern, you will collaborate with experienced data scientists to support analysts and subject matter experts in collecting and validating safeguards-relevant information. Your tasks will involve training and evaluating models across various data types, ensuring model explainability and robustness, and exploring the latest advancements in Artificial Intelligence.
Functions / Key Results Expected
As an intern within the SGIM Innovation Team, you will support the enhancement of analytical capabilities and receive significant professional development. Your tasks will include:
- Building rapid prototypes and proof-of-concepts to assess fit and usability.
- Continuously improving machine learning models, including text and image analysis, classification, similarity search, and named entity recognition.
- Interacting with business users to define scope and business cases.
- Developing and applying technologies such as machine learning and deep learning algorithms for advanced analytics.
- Identifying valuable data sources and improving efficiency in data collection, processing, and analysis tasks.
Qualifications and Experience
- University degree in data science, computer science with a relevant specialization, or a related field like applied mathematics, statistics, or computational science.
- Demonstrated skills in the analytic language Python.
- Experience in machine learning methodologies (e.g., supervised and unsupervised learning, deep learning) and understanding of large language model and multimodal model concepts.
- Knowledge of popular machine learning libraries and frameworks such as PyTorch, Transformers, and Hugging Face.
Applicant Eligibility
- Minimum of 20 years of age.
- Completed at least three years of full-time studies towards a first degree at a university or equivalent institution.
- May apply up to one year after completing a bachelor's, master's, or doctorate degree.
- Must not have previously participated in the IAEA's internship program.
- Good written and spoken English is essential; fluency in any other IAEA official language (Arabic, Chinese, French, Spanish, or Russian) is an asset.
- Must attach two signed letters of recommendation.
Key skills/competency
- Data Science
- Machine Learning
- Python
- Deep Learning
- Large Language Models
- Multimodal Models
- Generative AI
- Data Analysis
- AI
- Analytics
Skills & topics
- Data Science
- Analytics
- Internship
- Machine Learning
- Python
- Deep Learning
- Large Language Models
- Generative AI
- AI
- IAEA
How to get hired
- Tailor your resume: Highlight your Python, machine learning, and AI skills. Emphasize any experience with LLMs, multimodal models, or generative AI.
- Craft a strong cover letter: Express your passion for data science and explain why you are interested in the IAEA's mission and the specific role.
- Prepare for technical questions: Be ready to discuss your experience with Python libraries (PyTorch, Transformers, Hugging Face) and machine learning methodologies.
- Showcase your academic achievements: Mention your relevant university degree and any research projects or coursework related to data science or AI.
- Secure strong recommendations: Ensure your two letters of recommendation are from professors or supervisors who can attest to your skills and work ethic.
Technical preparation
Behavioral questions
Frequently asked questions
- What are the specific technical skills required for the Data Science and Analytics Intern role at the IAEA?
- The IAEA Data Science and Analytics Intern role requires demonstrated skills in Python for analytics, experience with machine learning methodologies (supervised/unsupervised learning, deep learning), and an understanding of large language models and multimodal models. Familiarity with libraries like PyTorch, Transformers, and Hugging Face is also crucial.
- How long is the Data Science and Analytics Internship at the International Atomic Energy Agency?
- The duration of the IAEA Data Science and Analytics internship is typically between three months and one year, as specified by the program guidelines.
- What is the eligibility criteria for the Data Science and Analytics Intern position at the IAEA?
- To be eligible for the Data Science and Analytics Intern position at the IAEA, candidates must be at least 20 years old, have completed at least three years of university study towards a degree, and not have participated in the IAEA internship program before. Applicants can apply up to one year after degree completion.
- Does the IAEA Data Science and Analytics Internship require specific language proficiency?
- Good written and spoken English is essential for the IAEA Data Science and Analytics Internship. Fluency in any other IAEA official language (Arabic, Chinese, French, Spanish, or Russian) is considered an asset but not mandatory.
- What kind of projects can I expect to work on as a Data Science and Analytics Intern at the IAEA?
- As a Data Science and Analytics Intern at the IAEA, you can expect to work on projects involving building prototypes, improving machine learning models for text and image analysis, applying deep learning algorithms, and identifying data sources to enhance analytical capabilities for safeguards information.
- What academic background is preferred for the Data Science and Analytics Intern at the IAEA?
- The preferred academic background for the Data Science and Analytics Intern at the IAEA includes a university degree in data science, computer science with a relevant specialization, or related fields such as applied mathematics, statistics, or computational science.
- Are letters of recommendation necessary for the IAEA Data Science and Analytics Internship application?
- Yes, two signed letters of recommendation are required for the IAEA Data Science and Analytics Internship application. These should come from individuals who can speak to your academic and professional capabilities.