Daniel Whitenack, Data Scientist at SIL, shares his expertise on building ML teams from the ground up, emphasizing the importance of gaining buy-in across the organization, proving value early through creativity and scrappiness, being a producer rather than a consumer with engineering teams, experimenting cross-functionally to identify highest impact areas, hiring owners over theorists, and overcoming failure by looping in end users early. He also highlights the advantages of startups' agility in leveraging pre-trained models and fine-tuning them for specific use cases.