『The Effective Data Scientist』のカバーアート

The Effective Data Scientist

The Effective Data Scientist

著者： Alexander Schacht and Paolo Eusebi
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Do you want to boost your career as a data scientist? Our podcast helps you in achieving this by teaching you relevant knowledge about all the different aspects of becoming a more effective data scientist.Alexander Schacht 博物学 科学 自然・生態学
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  • Your AI-Powered Data Science Toolkit

    Your AI-Powered Data Science Toolkit
    2026/10/22
    AI can dramatically expand what one data scientist can do. But where does that multiplier stop? Aziza sits down with Glen Wright Colopy of Polaris Data Labs to explore what AI-powered data science actually looks like in practice. They discuss how to choose and configure AI tools, when more capable models really matter, what can go wrong when AI is adopted without a clear framework, and whether AI can genuinely replace a data scientist. They also explore ROI, stakeholder trust, critical thinking, and the skills that will matter most as the profession evolves.
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    1 時間 4 分
  • The Secret Power of Data Evangelist

    The Secret Power of Data Evangelist
    2026/10/15
    In this episode of The Effective Data Scientist Podcast, we sit down with Jeff Stevenson, CMC MSAT Specialist at Boehringer Ingelheim and one of the most influential “Data Evangelists” in modern biopharma. Jeff shares how 30 years across Quality, Supply Chain, and MSAT shaped his philosophy on integrity, responsibility, and the true meaning of data readiness. Together, we explore the STIPS mindset, why most companies still fall into the IT Trap, how psychological safety drives technical excellence, and which AI tools are finally helping teams achieve analytic‑ready data. Jeff closes with a powerful reflection on what he would do differently if he were starting his career today.
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    47 分
  • Using Frameworks for Prompts for Better Outcomes

    Using Frameworks for Prompts for Better Outcomes
    2026/06/12
    How can you get better, more reliable results from AI? In this episode, Paolo and Aziza explore why effective prompting is about much more than asking good questions. We discuss how structured prompting frameworks such as ROSES, CRISP, CARE, TRACE, and APE can help data scientists provide clearer instructions, reduce ambiguity, and improve the quality of AI-generated outputs. We also share practical examples of applying these frameworks to real-world data challenges and discuss why designing how AI should think is often more important than the answer itself.
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    21 分
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