How to Shine as a Data Scientist in the Vibe Coding Era

在“氛围编程”时代，数据科学家如何脱颖而出

Here’s how to be the Data Scientist who thrives in a world where coding is a commodity. 在编程已成为一种廉价商品的时代，如何成为一名游刃有余的数据科学家？

Let’s create a fictional character: “Isaac“. Isaac lives in the early 80s. Isaac is an excellent architect, but he is a little set in his own ways. For example, he always says: “I don’t trust this calculator thing; I will still do all the computation by hand.” 让我们虚构一个角色：“艾萨克”（Isaac）。艾萨克生活在 80 年代初。他是一位出色的建筑师，但有些固执己见。例如，他总是说：“我不相信计算器这玩意儿；我还是会坚持手工完成所有计算。”

As a consequence, all the other architects in the firm deliver way more than he does because they use this innovative tool that he doesn’t trust and refuses to adopt. Keep in mind that Isaac is still essential to the architecture firm he works for: he can never be replaced by a calculator. 结果，公司里其他建筑师的产出远超于他，因为他们使用了他所不信任且拒绝采用的创新工具。请记住，艾萨克对所在的建筑事务所依然至关重要：计算器永远无法取代他。

However, it is a shame that Isaac’s talent is not fully used to develop new projects, unleash his creativity, and showcase his amazing work, simply because he refuses to adopt a productivity tool. The goal of this post is for you not to be that Isaac. 然而，遗憾的是，仅仅因为拒绝使用生产力工具，艾萨克的才华没能被充分利用来开发新项目、释放创造力或展示其卓越的作品。这篇文章的目的，就是让你不要成为那个“艾萨克”。

Step 0: Breathe!

第 0 步：深呼吸！

The people who are most overwhelmed by AI are the ones who work with it. If you feel that you are behind, and you don’t know enough, and everyone else is doing better than you, I want to give you this one piece of advice: take a deep breath. 最被人工智能压得喘不过气来的人，往往正是那些正在使用它的人。如果你感到自己落后了、知识储备不足，或者觉得别人都比你做得好，我想给你一个建议：深呼吸。

AI is changing and evolving at a speed where knowing every new LLM out there and every possible vibe-coded, agentic “revolutionary” tool being published on LinkedIn is not only impossible but also unnecessary and ultimately meaningless for your career. 人工智能的演进速度极快，想要了解市面上每一个新的大语言模型（LLM），以及 LinkedIn 上发布的每一个所谓的“氛围编程”或“革命性”智能体工具，不仅是不可能的，而且是不必要的，对你的职业生涯来说最终毫无意义。

Step 1: What are we dealing with?

第 1 步：我们面对的是什么？

Let’s start with this: Large Language Models are productivity tools. That’s all they are. They allow you to ship things fast; they optimize your time by reducing the amount of coding; they automate your recurrent flows and loops. 让我们从这一点开始：大语言模型就是生产力工具。仅此而已。它们能让你快速交付成果；通过减少编码量来优化你的时间；并自动化处理重复性的流程和循环。

What they are not is functioning Data Scientists who completely understand the logic of your business, who have all the pieces and information to navigate open-ended problems without definite and perfect solutions. 它们并不是能够完全理解业务逻辑、掌握所有信息以应对没有标准答案的开放性问题的数据科学家。

Step 2: Don’t delegate the thinking!

第 2 步：不要把思考外包！

Technically, we know that LLMs are amazing information retrievers but bad extrapolators. This means, for example, that an LLM can find information very quickly (and often correctly), but it won’t do a great job in writing a poem that doesn’t sound like it is a (mediocre at best) reshuffling of old poems. 从技术上讲，我们知道大语言模型是出色的信息检索者，但却是糟糕的推断者。这意味着，例如，LLM 可以非常快速（且通常准确）地找到信息，但它很难写出一首不像是（充其量平庸的）旧诗拼凑而成的诗歌。

My recipe is thus the following: Use LLMs to extract information and evaluate suggestions; use your brain to be creative and make sure all the requirements are met. This will ultimately make sure that your personality and sharpness will emerge and that people will remember you for the way you work. 因此，我的建议是：利用 LLM 来提取信息和评估建议；利用你的大脑去发挥创造力，并确保满足所有需求。这最终将确保你的个性和敏锐度得以展现，并让人们因为你的工作方式而记住你。

Step 3: Build your own system

第 3 步：构建你自己的系统

Not everyone uses AI the same way, but every user has their own preferences. The new advance from simple ChatGPT to systems like Claude Code, Codex, or Cursor is the ability to build your own AI routines. 并非每个人使用 AI 的方式都相同，但每个用户都有自己的偏好。从简单的 ChatGPT 到 Claude Code、Codex 或 Cursor 等系统的进步，在于你能够构建自己的 AI 工作流。

Through tools called “skills/commands,” “MCPs”, “loops”, and much more, you will have a real boost in productivity and also sound somewhat “unique”. 通过所谓的“技能/命令”、“MCP”、“循环”等工具，你不仅能获得生产力的真正提升，还会显得与众不同。