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The AI Dream Team: Strategies for ML Recruitment and Growth // Jelmer Borst and Daniela Solis // #267

Oct 9, 2024
58:42
Snipd AI
Jelmer Borst, analytics and machine learning leader at Picnic, and Daniela Solis Morales, machine learning lead, delve into the dynamics of building effective ML teams. They discuss shifting from decentralized to centralized structures and the challenges of recruiting the right talent. The pair explores the complexities of demand forecasting in online grocery delivery and stresses the importance of collaboration between data scientists and business teams. They also highlight the need for lightweight, scalable ML infrastructure and the evolving roles within data science to meet business goals.
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Podcast summary created with Snipd AI

Quick takeaways

  • Picnic transitioned from a decentralized to a centralized machine learning team, enhancing collaboration and efficiency in their projects.
  • The importance of a centralized data engineering team at Picnic significantly improved access to clean data, accelerating model development processes.

Deep dives

The Journey from Decentralized to Centralized Machine Learning

The machine learning team at Picnic transitioned from a decentralized to a centralized approach, contrasting with the experience of Delivery Hero, which started with a centralized model and later decentralized. Initially, Picnic's machine learning engineers faced challenges due to the decentralized model, especially regarding knowledge sharing and maintaining best practices. After recognizing the benefits of centralization, such as improved collaboration and efficiency, the team decided to consolidate efforts to enhance their overall machine learning capabilities. This restructuring allowed for quicker iterations on machine learning solutions and helped the team manage growing demands effectively.

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