Lessons from a nonlinear data science career in the age of AI
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In this episode, I chat with Kelly, a data scientist, lifelong learner, and author of A Friendly Guide to Data Science. They shared their nonlinear career path across multiple industries, their motivation for writing a beginner-friendly yet comprehensive data science book, and their perspectives on mentoring, data quality, soft skills, ethics, and the evolving role of AI.
We spent a lot of time discussing the realities of data science: messy data, stakeholder collaboration, domain knowledge, communication, and ethical responsibility. And of course, we talked about generative AI and why data science remains foundational for decision-making - human judgment, empathy, and experience cannot be automated away.
We covered many reasons why being a successful data scientist goes beyond technical depth and includes curiosity, adaptability, and respect for the people behind the data.
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Transcripts available on YouTube and Substack.
Topics Covered
- Nonlinear Career Paths in Data Science: Moving across industries and having a “wandering” career can create unique competitive advantages
- Why Data Science Still Matters in the Age of AI: Generative AI is powerful but overhyped - AI cannot replace data quality, context, or human judgment
- The Motivation Behind Writing a Data Science Book: There’s a gap in beginner-friendly, big-picture resources and an overemphasis on algorithms versus real-world workflows
- The Reality of Data Quality: “Garbage in, garbage out” is very common - most real-world data is not analysis-ready
- Soft Skills and Mentorship: Communication, empathy, and collaboration are career multipliers
- Ethics in Data Science: The need for empathy and awareness in technical decision-making
- Advice for Aspiring Data Scientists: Study job descriptions to guide learning and consider adjacent roles (product, engineering, analytics)