How to Level Up as a Data Scientist - Convo with Mark

Career advice for breaking into data analytics

Recently, I chatted with Prisha, a master’s student who is navigating the job market in data science & analytics. We talked about technical skills, networking, long-term career planning, and why it’s important to stay adaptable as the field evolves. Some of the key takeaways from the conversation: Breadth beats niche early on Projects should tell a business story Standing out requires more than academics Referrals matter, but only if you’re a strong candidate LinkedIn content should attract decision-makers Networking is a long game Stay adaptable as the industry shifts 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 [https://datastoryteller.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

How AI is Changing Data Analytics - Convo with a VP of Data

Chris Byington [https://www.linkedin.com/in/chris-byington/] has spent 15 years in analytics across consulting, startups, and tech companies, and the last decade leading teams. Recently, we sat down to chat about how analytics has evolved over this time, what he looks for when hiring, and how AI has impacted the role. Here are some of the highlights. AI’s biggest gift to analysts is headspace Of course, we had to talk about AI. Yes, AI can write your SQL, draft your weekly update, and build a visualization in minutes instead of hours. But Chris frames the real value differently: it’s not about saving time, it’s about protecting focus. “It gives you longer blocks to think deeply,” which is where the best analytical work actually happens. The clerical stuff was always getting in the way of that. AI tools for analytics are useless without a semantic layer AI-powered BI tools are overhyped, at least for the time being. Chris’s view: the SQL has never been the hard part. The hard part is knowing what the data actually means: which filters matter, which users to exclude, which date ranges to trust. Until that implicit knowledge is made explicit in a governed semantic model, natural-language queries will keep giving you plausible-looking wrong answers. Data teams are no longer seen as magicians 15 years ago, analytics was treated like a mysterious field full of magic. Today, having an analytics function is expected, and stakeholders know how to work together. The upside: clearer partnerships and better questions. The tradeoff is higher expectations than ever. This is true not just for hiring, but for the job itself. When AI speeds up analysis, the bottleneck is asking the right questions If a question that used to take a week can now be answered in 90 minutes, the constraint shifts entirely. Chris argues that data teams have historically been great at point problems like A/B tests and feature analysis, but now need to move upstream. “How much data actually factors into your executive team’s annual planning decisions? It scares me how little, sometimes.” That’s where the leverage is. Chris looks for two things when hiring - and neither is technical After 15 years of building teams, Chris has distilled his hiring filter to: Batteries included: Genuine drive to move the business forward without needing a lot of handholding. Coachability: The ability to receive feedback and actually grow from it. Technical skills are testable and trainable. Whether someone cares about impact, can take initiative and solve problems on their own (relative to their level and experience), and can take honest feedback? That’s much more important and much harder to train. Listen to the full episode for more on data governance, breaking into analytics in 2026, and how Chris thinks about goal-setting at a tech company. 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 [https://datastoryteller.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Nonlinear Paths to Data Science - Convo with Kelly

Recently, I chatted with Kelly [https://kellypvincent.tech/], a data scientist, lifelong learner, and author of A Friendly Guide to Data Science [https://link.springer.com/book/10.1007/979-8-8688-1169-2]. 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 [https://datastoryteller.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

How to Level Up as a Data Scientist - Convo with Mark

Recently, I met with Mark, a fellow data scientist, to answer questions about career development and the real-world practice of data science. 🗂️ Topics Discussed: * The value of a master’s degree in data science * Mentorship and career coaching * How data science teams identify work to do * Communication and collaboration with business stakeholders * Differences between analytics and data science roles * Managing large vs. small projects * Career-defining projects and demonstrating impact * Measuring business value beyond accuracy * Lessons learned and what I would do differently in my career * The importance of communication and asking for help * Returning to marketing analytics and finding satisfaction in hybrid roles 🧩 Key Takeaways * A master’s program provides structure, credentials, and connections — but isn’t the only path. * Mentorship and coaching accelerate growth and confidence. * Great data scientists co-create with the business — not just deliver outputs. * Communication is a superpower; it amplifies technical work. * The most valued projects are those that clearly improve business outcomes. * Career pivots and non-linear paths can become your greatest asset. Let me know what you think! 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 [https://datastoryteller.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Get Analytics Experience Even if You Aren't on the Data Team | My Chat with a Product Researcher

I talk with Rahma, a product researcher, about how to use analytics to understand the user experience as well as how to pivot into analytics from another role. We discussed: * How to contribute to sales-driven data problems when you come from a more academic and research background, especially when B2B metrics can be vague and overwhelming. * Solutions to common UX problems like friction and churn. * How to lean into a different background when trying to transition to analytics and market yourself as a uniquely qualified candidate. * The most important skills for breaking into analytics. * How to make your portfolio projects more impactful. 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 [https://datastoryteller.substack.com?utm_medium=podcast&utm_campaign=CTA_1]