🌟 GE Aerospace, a global leader in aviation and aerospace engineering, is hiring DT Interns & Data Science Interns for 2025 in Bengaluru. This internship is a golden opportunity for B.Tech and M.Tech students to work on cutting-edge technologies like Machine Learning, Deep Learning, Generative AI, and Data Science. At GE Aerospace, interns gain hands-on experience in software engineering, digital solutions, and exploratory data science projects. If you are passionate about technology, programming, and innovation, this GE Aerospace Internship 2025 in Bangalore is the perfect way to kickstart your career in engineering and data science.
📌 Job Details
✨ Job Role: DT Intern & DT – Data Science Intern
✨ Qualification: Bachelor’s or Master’s in Engineering (CS/IT/Data Science/AI)
✨ Experience: Freshers
✨ Batch: B.Tech (3rd/4th Year) or M.Tech (1st/2nd Year)
✨ Salary: ₹3.3 – ₹5 LPA (Estimated)
✨ Job Location: Bengaluru
✨ Last Date to Apply: ASAP
🎓 Eligibility Criteria
✅ B.Tech 3rd/4th Year or M.Tech 1st/2nd Year students from reputed institutes
✅ Specialization in CS/IT/Data Science/AI preferred
✅ Minimum 7 CGPA / 70% with no active backlogs
✅ Strong skills in Python, ML, and data-driven problem-solving
✅ NOC from college authorities before offer issuance
💻 Job Description
🔹 Work on exploratory projects in Data Science, AI, and Machine Learning
🔹 Collaborate with mentors on real-world aerospace and digital solutions projects
🔹 Analyze, design, test, and integrate software components
🔹 Document and present technical data according to GE Aerospace standards
🔹 Stay updated with trending software and industry practices
🔹 Apply GenAI, deep learning, and modern data science techniques to solve practical problems
🌟 Why Join GE Aerospace?
💡 Joining GE Aerospace means working with a global team of innovators shaping the future of aviation. As an intern, you will:
✨ Gain exposure to world-class aerospace engineering projects
✨ Work at the John F. Welch Technology Center, Bengaluru – a hub for aerospace innovations
✨ Get mentored by experienced engineers and scientists
✨ Build strong skills in Data Science, Python, ML, and AI technologies
✨ Boost your career opportunities in engineering, aviation, and software industries
🏢 About GE Aerospace
GE Aerospace is a global leader in aviation, data science, and aerospace engineering solutions, advancing aerospace technologies for future generations. With its John F. Welch Technology Center (JFWTC) in Bengaluru, GE Aerospace provides students and freshers opportunities to work on machine learning, deep learning, AI, and software engineering projects in a world-class research environment.
📢 Other Information
🔸 Internship Type: Full-time
🔸 Industry: Aerospace, Data Science, IT Software
🔸 Work Mode: Onsite (Bengaluru) – Some remote opportunities available
🔸 Duration: Typically 3–6 months
🔸 Note: Internship does not guarantee a full-time job offer
📝 How to Apply?
All eligible and interested candidates can apply online using the official GE Aerospace links below:
👉 Apply for Data Science Intern: Click Here
👉 Apply for DT Intern: Click Here
❓ Interview Questions & Answers (Role-Based)
Q1: What is the difference between supervised and unsupervised machine learning?
👉 Supervised ML uses labeled data to train models, while unsupervised ML works on unlabeled data to find hidden patterns or clusters.
Q2: How would you handle missing data in a dataset?
👉 Techniques include removing missing values, replacing with mean/median/mode, or using advanced methods like regression and KNN imputation.
Q3: What is overfitting in machine learning and how can it be prevented?
👉 Overfitting happens when a model learns noise instead of patterns. Prevent it using cross-validation, dropout, regularization, or more data.
Q4: Explain the difference between precision and recall.
👉 Precision = Correct Positive Predictions / Total Predicted Positives, Recall = Correct Positive Predictions / Actual Positives. Precision measures accuracy, recall measures coverage.
Q5: How would you optimize a Python program that runs very slowly?
👉 Use efficient data structures, vectorization with NumPy, profiling tools, parallel processing, and algorithm optimization.
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