About Company:

Airbus pioneers sustainable aerospace for a safe and united world. As a global leader in aerospace, we have grown a rich local heritage over 50 years of presence in India, fostering a corporate culture driven by innovation, collaboration and excellence. We are constantly searching for talented people to expand our teams and contribute to our business in this vibrant country.
Our impact in India goes beyond boosting the local economy. It also plays a role in supporting social initiatives that have a positive effect on the lives of the communities, such as empowering women working in aviation.

Job Profile: Data Scientist Intern

Degree Required: B.E/B.Tech/B.Sc/M.E/M.Tech/M.Sc

Experience Required: Freshers

CTC: Best in Industry

Work Location: Bangalore

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).

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