AIRBUS is hiring Freshers candidates for INTERN – DATA SCIENTIST. The details of the job, requirements and other information given below:
AIRBUS IS HIRING : INTERN – DATA SCIENTIST
- Qualification : Any Bachelor’s /master’s Degree
- 2024/2025 Batches can apply
- 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).
- Location: Bangalore
Don’t miss out, CLICK HERE (to apply before the link expires)
Interview Questions & Answers for Data Scientist Intern – Airbus
Technical Interview Questions
1. What is Machine Learning, and how is it different from traditional programming?
Answer:
Machine Learning (ML) is a branch of artificial intelligence where a computer system learns patterns and makes predictions based on data, instead of being explicitly programmed with fixed rules. In traditional programming, developers write specific instructions for the computer to follow. For example, if we want a program to add two numbers, we write the rule: sum = a + b.
In contrast, ML systems learn from data. For instance, if you give an ML model lots of data on how the weather affects sales, it can learn to predict future sales without being directly told how to do it.
This learning happens through algorithms that adjust based on input data, and the model gets better as it is exposed to more data. This ability to improve with experience is what sets machine learning apart.
2. Explain the difference between supervised and unsupervised learning.
Answer:
Supervised Learning: In supervised learning, the model is trained on labeled data, meaning the input data has corresponding correct answers (labels). For example, if you are building a model to predict house prices, the data might include features like house size, number of rooms, etc., and each data point has a known price. The model learns to predict the price based on this input-output mapping. Common algorithms for supervised learning include linear regression, logistic regression, and decision trees.
Unsupervised Learning: In unsupervised learning, the model works with unlabeled data, meaning there are no predefined answers. The goal is to find hidden patterns or groupings in the data. For instance, in clustering (like K-means clustering), the model identifies groups of similar data points without knowing beforehand what those groups are. This type of learning is often used for customer segmentation or anomaly detection.
3. What is feature engineering, and why is it important?
Answer:
Feature engineering is the process of transforming raw data into meaningful features that better represent the problem you’re trying to solve. It involves creating new features, removing irrelevant ones, or transforming data so that the machine learning model can learn more effectively.
For example, if you’re working with date data, you might convert the date into day of the week or month because these might be more useful for your model than the raw date itself. Feature engineering is important because the quality and relevance of the features directly influence the performance of the machine learning model.
4. Explain the concept of overfitting in machine learning.
Answer:
Overfitting happens when a machine learning model becomes too complex and learns not just the patterns in the training data but also the noise (random fluctuations or irrelevant details). This means the model performs very well on the data it was trained on but fails to generalize to new, unseen data.
Imagine memorizing answers to specific questions instead of learning the underlying concepts. You would do well on the test with those specific questions but would struggle with different ones. In machine learning, overfitting can be prevented using techniques like cross-validation, pruning trees, or regularization.
5. Can you explain what a confusion matrix is?
Answer:
A confusion matrix is a tool used to evaluate the performance of a classification model. It shows the actual vs predicted classifications for a test dataset.
It consists of four parts:
True Positives (TP): Correctly predicted positive cases.
False Positives (FP): Incorrectly predicted positive cases.
True Negatives (TN): Correctly predicted negative cases.
False Negatives (FN): Incorrectly predicted negative cases.
From this matrix, you can calculate various metrics like accuracy, precision, recall, and F1-score. These help determine how well the model is performing in classifying the data correctly.
6. What are some commonly used Python libraries in data science, and how do they help?
Answer:
Some commonly used Python libraries for data science are:
NumPy: Helps with numerical computing and working with arrays.
Pandas: A powerful library for data manipulation and analysis. It allows you to handle data in tables (dataframes), perform operations like filtering, merging, and aggregating data.
Matplotlib & Seaborn: These are used for data visualization. With these, you can create charts, graphs, and plots to understand the patterns in your data.
Scikit-learn: This library contains many machine learning algorithms like regression, classification, and clustering. It’s great for building and evaluating models.
TensorFlow and PyTorch: These libraries are used for deep learning. They offer tools to build complex neural networks.
Behavioral Interview Questions
1. Why do you want to work as a Data Scientist Intern at Airbus?
Answer:
I am passionate about using data to solve real-world problems, and I am particularly interested in how machine learning and artificial intelligence are transforming industries. Airbus, as a leader in aviation and aerospace, offers a unique opportunity to apply data science to challenging, large-scale problems. I am excited about the chance to learn from experienced data scientists and engineers while contributing to meaningful projects in an innovative environment.
Additionally, I admire Airbus’s commitment to diversity and inclusion and its mission of promoting sustainability through technologies like Generative AI. This internship is a perfect fit for me to grow both personally and professionally.
2. Tell me about a time when you had to learn something new quickly. How did you approach it?
Answer:
During a university project, I was tasked with analyzing a large dataset using machine learning algorithms, but I had limited experience with those techniques. I first took an online course and read documentation on the algorithms I needed to use. Then, I practiced with smaller datasets to get hands-on experience. When I encountered challenges, I turned to online forums and discussions for guidance. Ultimately, I was able to apply what I learned to build a functional model, and it was a rewarding experience that taught me how to quickly pick up new skills under pressure.
3. How do you prioritize your tasks when working on multiple projects?
Answer:
When working on multiple projects, I first break down each project into smaller tasks and assign deadlines based on priority. I use tools like Trello or Google Calendar to organize my work and ensure I stay on track. I also communicate regularly with my team to ensure we’re aligned on expectations and deadlines. If I ever face bottlenecks, I assess which task has the most impact or urgency and tackle that first. Managing time effectively and staying organized has helped me juggle multiple priorities successfully.
4. How do you approach problem-solving when faced with a difficult data-related issue?
Answer:
When facing a difficult data-related issue, I first ensure that I fully understand the problem and gather as much information as possible. I then break the problem down into smaller, manageable parts and tackle them one by one. If necessary, I consult online resources, colleagues, or mentors for insights. Once I have a clear path, I implement a solution step by step and test it regularly to ensure it’s working. I also remain flexible and adjust my approach if I encounter new challenges.
Final Tips for the Interview:
Be curious and enthusiastic about learning new tools and concepts.
Demonstrate a passion for data science and how it can solve real-world problems.
Prepare questions for the interviewers about their data projects, team structure, and company culture.
Show a strong desire to grow and develop your skills within Airbus.
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