Recruiter's Glossary

Examples: Hyperparameter Tuning • Underfitting • Scikit-learn

Neural Networks

Term from Data Science industry explained for recruiters

Neural Networks are computer systems designed to learn and make decisions similarly to how a human brain works. They're a key part of artificial intelligence and machine learning that helps computers recognize patterns, make predictions, and solve complex problems. Think of them as digital brains that can be trained to do specific tasks like identifying objects in photos, predicting customer behavior, or translating languages. When you see this term in a resume, it usually means the candidate has experience in building or working with these smart computer systems to solve business problems. Similar terms you might see include "Deep Learning," "Machine Learning," or "AI Models."

Examples in Resumes

Developed Neural Network models to predict customer churn with 85% accuracy

Implemented Neural Networks and Deep Neural Networks for image recognition in retail applications

Led team of 3 data scientists in creating Neural Network solutions for fraud detection

Typical job title: "Neural Network Engineers"

Also try searching for:

Machine Learning Engineer AI Engineer Deep Learning Engineer Data Scientist AI/ML Engineer Neural Network Developer Machine Learning Researcher

Where to Find Neural Network Engineers

Online Communities

Job Boards

• AI-Jobs.net
• Machine Learning Jobs on Stack Overflow
• LinkedIn (tag: Neural Networks)

Events & Conferences

Professional Networks

Example Interview Questions

Senior Level Questions

Q: How would you explain the business value of neural networks to non-technical stakeholders?

Expected Answer: A senior candidate should be able to translate technical concepts into business benefits, discussing real ROI examples, cost savings, and efficiency improvements. They should mention specific use cases like customer prediction models or process automation without using technical jargon.

Q: How do you approach building and leading an AI project team?

Expected Answer: Should demonstrate project management skills, ability to bridge technical and business requirements, experience in setting realistic timelines, and managing stakeholder expectations. Should discuss how they handle resource allocation and team skill development.

Mid Level Questions

Q: What factors do you consider when choosing between neural networks and other machine learning approaches?

Expected Answer: Should be able to explain practical considerations like data availability, computing resources, project timeline, and business requirements in simple terms. Should demonstrate understanding of when neural networks might be overkill.

Q: How do you ensure the reliability and fairness of neural network models?

Expected Answer: Should discuss testing procedures, data quality checks, bias prevention, and model validation in business-friendly terms. Should show awareness of ethical considerations and model transparency.

Junior Level Questions

Q: What tools and frameworks have you used for building neural networks?

Expected Answer: Should be familiar with common tools like TensorFlow or PyTorch, and able to explain basic model building and training processes in simple terms.

Q: Can you describe a simple neural network project you've worked on?

Expected Answer: Should be able to explain their role in a project, the problem they solved, and the results achieved, focusing on practical applications rather than technical details.