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Intern - Data Scientist

Airbus

Updated on: 18 May 2025

Additional Details

Website

www.airbus.com

website

Work Location

Bengaluru, India

location

Job Type

Internship + Fte

job_type

Batch

2026 | 2025 | 2024

batch

Stream Required

Bachelors/Masters degree in Computer Science, Statistics, Mathematics, Data Science

stream

Salary

12-15 LPA (Expected)

salary

Job Description

We are seeking a highly motivated and enthusiastic Data Scientist Intern with a strong interest in Machine Learning to join our growing team. This internship offers an excellent opportunity for a student or recent graduate with limited professional experience to gain hands-on experience in developing, implementing, and evaluating machine learning models for real-world problems. While the primary focus will be on core ML techniques, exposure to and a basic understanding of Generative AI concepts will be considered a valuable asset. You will work alongside experienced data scientists and engineers, contributing to impactful projects and expanding your skills in a supportive and collaborative environment.

Responsibilities:

  • Data Exploration and Preprocessing: Assist in collecting, cleaning, and preparing data for machine learning tasks. This includes handling missing values, identifying outliers, and performing feature engineering.

  • Machine Learning Model Development: Learn and apply various machine learning algorithms (e.g., regression, classification, clustering) to solve specific business problems under the guidance of senior team members.

  • Model Evaluation and Validation: Participate in evaluating model performance using appropriate metrics and validation techniques to ensure robustness and generalization.

  • Experimentation and Iteration: Assist in designing and executing experiments to test different models and hyperparameter configurations.

  • Documentation and Communication: Document code, methodologies, and results clearly and concisely. Participate in team meetings and present findings as required.

  • Learning and Skill Development: Actively learn new machine learning techniques, tools, and best practices through training materials, mentorship, and independent research.

  • Exposure to Generative AI (Good to Know): Participate in discussions and potentially assist with exploratory projects related to Generative AI, such as understanding basic concepts, exploring pre-trained models, or assisting with data preparation for GenAI tasks (if applicable).

  • Collaboration: Work effectively with data scientists, engineers, and other stakeholders to understand project requirements and contribute to team goals.

Qualifications:

  • Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.  

 

  • Strong foundational knowledge of mathematics (linear algebra, calculus, probability) and statistics.

  • Familiarity with programming languages commonly used in data science, particularly Python.

  • Basic understanding of core Machine Learning concepts and algorithms (e.g., linear regression, logistic regression, decision trees, random forests, k-means clustering).

  • Experience with relevant Python libraries such as NumPy, Pandas, Scikit-learn, and Matplotlib/Seaborn is a plus.

  • Strong analytical and problem-solving skills with the ability to interpret data and draw meaningful conclusions.

  • Excellent communication (both written and verbal) and interpersonal skills.

  • Ability to learn quickly and adapt to new technologies and methodologies.

  • A proactive attitude and a strong desire to learn and contribute to real-world projects.

Good to Know:

  • Basic understanding of Generative AI concepts and models (e.g., Large Language Models (LLMs), diffusion models).

  • Familiarity with any deep learning frameworks (e.g., TensorFlow, PyTorch).

  • Experience with version control systems (e.g., Git).

  • Exposure to cloud computing platforms (e.g., AWS, GCP).

 

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.

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