Conduct disciplined experiments to test and validate models. Design experimental frameworks, use train validation test splits and cross validation, and interpret results with appropriate statistical significance and confidence intervals.
Feature engineering rooted in business and statistical understanding. Create informative features through aggregation, encoding, interaction terms, and time windows; assess feature importance and stability over time.
Model evaluation using statistically sound metrics. Evaluate with precision, recall, F1 score, ROC AUC, calibration, confusion matrices, and cost sensitive metrics appropriate to the problem.
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Data Organization: Keep our internal e-commerce and customer database structured and clean, ensuring our AI experiments have high-quality data to learn from.
Develop and implement machine learning models (regression, classification, clustering) to predict maintenance needs and optimize operations.
Leverage Large Language Models (LLMs) to enhance data products through automated feature extraction, data enrichment, and intelligent information retrieval and decision making.
Create scalable data solutions that can handle real-time aircraft sensor data and maintenance logs.
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Deployment & Automation Support: Work with tools like Docker to streamline deployment processes, automate workflows, and maintain efficient CI/CD pipelines.
System Maintenance & Optimization: Monitor system performance, troubleshoot issues, and implement optimizations to ensure high availability and scalability of AI applications.
Prototype & Experimentation: Participate in prototyping new AI technologies and solutions, staying up-to-date with industry advancements and exploring creative ways to apply AI in our projects.