Feature Engineering
Feature Engineering is the process of making raw data more useful for creating predictions and insights. Think of it like a chef preparing ingredients before cooking - data scientists take raw information and transform it into a format that computers can better understand and work with. For example, they might take dates and convert them into more meaningful information like "day of week" or "is this a holiday?" This preparation work is crucial for making machine learning models work better, just like well-prepared ingredients make a better meal.
Examples in Resumes
Improved model accuracy by 30% through Feature Engineering of customer behavior data
Applied advanced Feature Engineering techniques to transform raw sensor data into meaningful patterns
Led Feature Engineering efforts for predictive maintenance project, reducing equipment downtime by 25%
Typical job title: "Data Scientists"
Also try searching for:
Where to Find Data Scientists
Online Communities
Job Boards
Professional Networks
Example Interview Questions
Senior Level Questions
Q: How do you approach feature engineering for a new project with messy, raw data?
Expected Answer: A senior should explain their systematic approach to understanding the data first, then creating meaningful features based on business context, including handling missing data, creating interaction features, and validating the impact of new features on model performance.
Q: Can you describe a complex feature engineering challenge you solved and its impact?
Expected Answer: They should share a specific example showing how they transformed difficult data into useful features, explaining their thinking process and how they measured success in business terms.
Mid Level Questions
Q: What are common feature engineering techniques you use with categorical data?
Expected Answer: Should explain basic approaches like converting text categories into numbers, handling multiple categories, and creating new meaningful groupings based on data patterns.
Q: How do you handle time-based data in feature engineering?
Expected Answer: Should describe ways to make date/time information useful, like creating features for seasons, business hours, or time between events.
Junior Level Questions
Q: What is feature scaling and why is it important?
Expected Answer: Should explain that feature scaling means adjusting numbers to be in similar ranges so the computer can process them better, like converting prices and ages to similar scales.
Q: How do you handle missing data in features?
Expected Answer: Should describe basic approaches to dealing with missing information, such as filling in with averages or most common values.
Experience Level Indicators
Junior (0-2 years)
- Basic data cleaning and preprocessing
- Simple numerical and categorical transformations
- Understanding of common data types
- Basic statistical calculations
Mid (2-4 years)
- Advanced data transformation techniques
- Time series feature creation
- Automated feature generation
- Feature selection methods
Senior (4+ years)
- Complex feature engineering pipelines
- Domain-specific feature creation
- Performance optimization
- Team leadership in feature engineering projects
Red Flags to Watch For
- No experience with real-world messy data
- Lack of understanding of basic statistics
- Unable to explain why certain features might be useful
- No knowledge of automated feature selection techniques
- Poor understanding of data cleaning basics
Related Terms
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