Zebra Technologies Data Scientist Interview Questions and Answers

Model Development

• Train, fine-tune, and deploy Large Language Models (LLMs)
• Build and maintain scalable machine learning workflows
• Optimize model performance and mitigate biases
• Conduct independent research and innovation in AI

Data Pipeline Management

• Design and optimize ETL pipelines using PySpark and Databricks
• Develop automated data validation processes
• Implement cloud resource allocation to control costs
• Ensure efficient data ingestion and model training

Collaboration and Communication

• Engage with customers to understand their needs
• Explain model outputs and data deficiencies to stakeholders
• Collaborate with cross-functional teams seamlessly
• Document and articulate findings clearly

Technical Implementation

• Utilize MLOps tools for model deployment
• Implement best practices in coding and architecture
• Leverage cloud platforms for model training and serving
• Perform experiments to identify model improvement opportunities