A detailed, up-to-date walkthrough for implementing language models in data science applications In Data Science First: Using Language Models in AI-Enabled Applications, the Chief AI Officer at Intersect AI, John Hawkins, sets out the critical challenge facing data scientists today: how to effectively integrate powerful language models into their workflows while adhering to data science principles that ensures your data generates reliable conclusions. Hawkins provides a practical roadmap for leveraging these revolutionary tools while maintaining the analytical rigor that separates successful implementations from costly failures. This guide skips hype and jargon, focusing instead on nine proven strategies for applying language models in real-world data science projects. From exploiting semantic vectors and few-shot prompting to synthetic data generation and developing agentic AI applications, Data Science First presents concrete design patterns that remain relevant despite rapidly evolving technologies. Each approach is illustrated with detailed case studies, including complaint processing and resume filtering, demonstrating how to evaluate model performance, handle failure modes, and deliver measurable business value. Data Science First is perfect for data scientists interested in enhancing their traditional statistical and machine learning skills with modern AI capabilities. It's also a must-read for software engineers building language model-powered products and technical managers interested in deploying these tools reliably.