Data Science Intern for Airframe Structures
Team: Structures - Aerostructures Data Management
Background :
Enable advanced digital capabilities in the perimeter of Airframe related Design, Modeling, Simulation, Testing and Inspection along the lifecycle of Aircraft development using Data Analytics / Artificial Intelligence / Machine Learning.
Role :
The Intern will act as a Data Scientist focusing on Airframe related applications with the task to provide safe and balanced data driven design solutions. They will play a key role in developing machine learning and deep learning (classification, regression, time-series and natural language processing including Generative AI) models and/or analytics dashboards & stand-alone web applications to analyze aircraft structure s operational reliability and build predictive and prescriptive models that assist in modeling, simulation and automation. They need to engage in tasks related to data engineering, knowledge graph creation, chatbot building, tuning and monitoring.
Technical Skills
Key Skills
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Programming Proficiency: Foundational knowledge and hands-on experience in Python. Familiarity with basic data manipulation libraries like Pandas and statistical packages.
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Machine Learning (ML) Basics : Basic understanding of supervised learning concepts (Classification, Regression), and exposure to Deep Learning (DL) or Neural Networks is a plus.
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NLP & Generative AI Curiosity : Strong interest in Natural Language Processing (NLP) and Large Language Models (LLMs). Project experience involving text analysis or LLMs is highly desirable.
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RAG System Exposure : Demonstrated understanding or project experience with Retrieval-Augmented Generation (RAG) architecture, vector databases, and advanced context-based querying.
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Model Evaluation : Basic statistical knowledge for analyzing model performance and reliability.
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Visualization : Ability to create simple yet clear dashboards or demonstrations using tools like Streamlit, Dash, or similar environments (e.g., academic projects in Palantir/Skywise).
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Data Driven Thinking : Demonstrated ability to approach a problem by asking concrete analytical questions and seeking data-based insights.
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Cloud & Deployment Awareness: Basic familiarity with cloud concepts (AWS, Azure, or GCP) and the principles of deploying code (MLOps).
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Prompt Engineering: Understanding of how to structure inputs (Prompt Engineering) to get the best results from AI tools.
Good to have (not mandatory)
Basic understanding of System Engineering and Aerospace/ Aviation
Basic knowledge on model distillation of LLMs
Basic Knowledge on finetuning & performance optimization of LLMs
Exposure to Software testing; Verification, Validation & Uncertainty Quantification of ML
Preferred Language: Python
Data Science Frameworks: Scikit-Learn, Keras, Tensorflow, Pytorch, Langchain etc.
Environments: Jupyter Notebook / Anaconda, Visual Studio etc.
Soft Skills:
Teamwork and excellent communication skills
Willingness to learn new skills, self-development and Problem-solving orientation
Fail-fast mindset and Solutions approach
Qualification : Pursuing (final year) or completed Masters or Bachelors degree in Aerospace, Mechanical, Thermal or Data Science related streams.
Job Location : Bangalore
This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company s success, reputation and sustainable growth.
Company:
Airbus India Private Limited
Employment Type:
Internship
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Experience Level:
Student
Job Family:
Structure Design & Integration
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Airbus is committed to achieving workforce diversity and creating an inclusive working environment. We welcome all applications irrespective of social and cultural background, age, gender, disability, sexual orientation or religious belief.
Airbus is, and always has been, committed to equal opportunities for all. As such, we will never ask for any type of monetary exchange in the frame of a recruitment process. . Wherever possible, we foster flexible working arrangements to stimulate innovative thinking.
