Recently, I chatted 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. 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) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datastoryteller.substack.com
