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Why product DS, applied DS, and AI engineering roles are exploding right now.Subscribe|Submit a tip|Advertise with Us🧩Welcome to DataPro 159. Our Expert Insight this week features Walmart Senior Data Scientist Karun as he breaks down how AI, automation, and GenAI are rewriting the rules of data science. Whether you are job hunting or leveling up your skills, his advice cuts straight to what matters. We are also bringing something new to our readers, straight from experts with first-hand industry experience. Packt has launched live AMA sessions with industry leaders on the Packt DataML YouTube channel, hosted by our Growth Lead Abhishek Kaushik. These sessions give you direct access to practical guidance, career clarity, and real world insights from voices shaping the future of Python, AI, and data science.In this edition, we have included Karun’s take on evolving data science roles, portfolio strategies that stand out, and the skills hiring managers value most in 2025 and 2026. You will find the extended highlights below.Knowledge Partner Spotlight: OutskillAt Packt, we’ve partnered withOutskillto help readers gain practical exposure to AI tools through free workshops, complementing the deeper, hands-on, expert-led experiences offered throughPackt Virtual Conferences.Still doing manual tasks repetitively? Learn AI to automate 50% of your work.Outskill is hosting a 2-Day Live AI Mastermind, an intensive training on AI tools, automations, and agent building designed to make you indispensable. As part of their Holiday Season Giveaway, the first 100 participants can join for free, even though the program normally costs $395. 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Pick a Full Pass or VIP Pass, use FINAL20 for 20% off, and reserve your seat.Register Now and Save With the Early Bird OfferCheers,Merlyn ShelleyGrowth Lead, PacktThe Real Story Behind Today’s Data Science Job Market and How to Stand Out with Karun ThankachanIf you feel like the data science world is shifting under your feet, you are not alone. In the latest episode of Pack Talks, Abhishek Kaushik sat down with Karun, a senior data scientist at Walmart, to unpack what it really takes to break into and thrive in this field today. The conversation was honest, pragmatic, and sprinkled with hard-won wisdom from someone who has navigated one of the toughest job markets in recent history.This article brings you the essential takeaways: how the role of a data scientist is evolving, what skills truly matter now, how to build a competitive portfolio, and the strategies that actually work for landing interviews and surviving them.Let’s dive in.From Pandemic Turbulence to Walmart: Karun’s Nonlinear JourneyKarun’s story begins like many hopeful data scientists. He moved to the US for his master’s at Carnegie Mellon, rode the initial excitement of landing an internship, then watched that offer disappear during the COVID downturn. It was a moment when thousands of fresh graduates questioned whether the field still had space for them.Instead of giving up, he leaned into referrals, rebuilt his pipeline, and secured a data science internship at Amazon. That later became a full-time role, and today he works on high-impact data science initiatives inside Walmart.His journey is a reminder that resilience still matters. The field is changing, but it is far from closing its doors.Is Data Science Still a Good Career? The Market Has Evolved, Not ShrunkWhen asked the million-dollar question, Karun was clear. The career is still viable, but the expectations have shifted.Here is how the field has changed since 2017.1. Model building has become easierThe rise of tools like XGBoost and transformers reduced the complexity of many modeling tasks. You no longer need niche mathematical expertise to build a model that works.2. Business impact has become the differentiatorHiring managers want people who can translate problems into solutions that move metrics, reduce costs, or improve customer experience.3. Entry-level roles now demand mid-level thinkingThanks to automation and AI assistants, junior roles expect stronger system design, problem framing, and measurable impact.4. Career growth now mirrors software engineeringInstead of staying at the “modeling” layer, data scientists are expected to grow into architecture, experimentation strategy, and problem definition.The bottom line: AI did not remove data science jobs. It elevated the bar for what a data scientist does.The Four Data Science Roles You Need to Understand in 2025Karun breaks down today’s landscape into four categories. Knowing the difference helps you target your learning, resume, and projects.Role TypeWhat You Actually DoHow AI Affects ItProduct Data ScientistA/B testing, causal inference, experimentationStill high in demand. Requires sound statistical reasoning.Applied Data ScientistBuild customer-facing models and deploy pipelinesCoding and model tuning remain essential. Not heavily automated yet.Research Data ScientistCreate new model architectures and algorithmsLeast affected by automation. Requires deep ML expertise.AI Engineeroptimize LLM pipelines, work with vector DBs, prompts, deployment. Growing rapidly as companies scale GenAI.Inside Walmart, Karun shared that they already integrate ChatGPT-powered workflows internally. But even with AI tools in the loop, human oversight, system thinking, and engineering maturity remain essential.Python vs R in 2025: The Debate Is OverKarun did not hesitate here.Python has won.Why:It integrates with big data tools like PySparkIt is production friendlyIt is the default language in ML teamsIt has unmatched community supportR is still relevant for specialized statistical work, but new entrants are better off starting with Python. For performance heavy systems, C and C++ still matter, especially for latency sensitive pipelines.Want Recruiters To Notice You? Build Business Oriented ProjectsKarun’s portfolio advice is refreshingly practical.Stop doing random datasets. Start doing business problems.He recommends:Kaggle competitions with prize moneyRetail and financial datasets like M5 forecasting, H&M recommendations, Jane Street market predictionProjects where you quantify your performance against leadersOne golden rule:Always show how close you are to rank one. Not just your accuracy.👉 Continue reading the full article on the Packt Medium handle.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

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