--- author: 课代表立正 date: '2026-11-10' guest: '' layout: post.njk source: https://www.youtube.com/watch?v=DKOQkkNdXQo speaker: 课代表立正 tags: - data-science - product-growth - experimentation - ai-transformation title: 数据科学家的定位转变：从分析师到产品构建者 summary: 嘉宾探讨了数据科学与分析在产品增长中的角色演进。内容涉及写书动机、PLG飞轮模型、以留存作为PMF核心指标、实验100%覆盖率的价值、数据讲故事与图表设计原则，以及AI时代数据人应具备自主性、品味并转型为直接交付功能的构建者。 insight: '' draft: true series: '' category: ai-application area: tech-engineering project: [] people: [] companies_orgs: [] products_models: [] media_books: [] status: evergreen --- ### 初级数据科学家的就业现状与角色本质 当时挂出了一个初级数据科学家的职位，这在当下其实相当少见。大家现在都不再招初级数据科学家了。我想我们在一天之内就收到了大约3000份申请。20年前根本没有数据科学家这个职位。数据科学家是一个被发明出来的职衔。它不像农民，对吧？它甚至不像清洁工。它是一个人为创造出来的角色。
Original English static uh put up a position for junior uh data scientist which was actually quite rare. People do do not hire junior data scientist anymore. We got I think 3,000 applications within a day. Uh there was no data scientist 20 years ago. Data scientist is a invented job title. It's not like a farmer, right? It's not like a toilet cleaner even. It's a invented role.
>> 所以我当时一直在思考的问题是：如果公司把所有做数据分析的人都开除，对吧？公司实际上会遭受损失吗，还是能继续维持下去？
Original English >> So the question I was wrestling with was what if they fire everyone uh in in analytics, right? Will the company actually lose anything or will they continue to continue to sustain?
>> 我总是告诉数据科学家，不要再只当个数据科学家了［笑声］
Original English >> I always tell data scientists to stop being a data scientist [laughter]
>> 而是［清嗓子］真正去成为一名构建者（builder）
Original English >> and to [clears throat] be a builder actually
>> 每个人都能做每个人的工作，就像全员随时待命协同作战一样。在这个产品或竞争环境中，你们认为脱颖而出的最独特或最好的技能是什么？
Original English >> and everyone can do everyone's job like all hands on the deck all the time. How do you guys think about the the unique or the best skill that you think stand out among this like product or competitive space?
### 开场介绍与写书初衷 >> 我只想说，非常荣幸能被邀请参加这次活动。通常大家可能会说“聊聊管理，聊聊这些”，但自从创立 Sundial 以来，我的目标实际上就是被数据与分析社区接纳……他是数据与增长领域极具影响力的领军人物。我们都知道他建立了一个非常棒的倡导者社群，他曾在腾讯、Meta 和亚马逊任职，中文播客及内容频道拥有30万关注者。他现在对 AI 教育抱有极大热情，也是实验领域的重要领军声音之一。
Original English >> I just want to say that I'm actually very honored to be invited to an event. Um often you know people are like oh you know talk about management and talk about this but [clears throat] it's actually my goal since starting Sundial to be accepted as part of the data and analytics community Dr. reasons and um he is a super prominent voice in the data and growth community. Um we all know that he's got an incredible uh advocate community member with um the the way that he has um uh been at 10 cent Meta and Amazon and he has 300,000 followers for his content channel for his Chinese podcast. Um he is super passionate now about AI education as well as uh been one of the great leading voices for experimentation.
>> 好的，让我们言归正传。
Original English >> All right, let's get down to brass tax.
>> 那么，是什么让你决定我们大家都需要这本书，并促使你动笔去写的呢？
Original English >> So what made you decide that we all needed this book that you decided to write?
>> 好的。我一直在思考的问题就是：如果他们把所有做数据分析的人都开除，对吧？公司到底会失去什么，还是能继续维持下去？在苦思这个问题几个月后，我意识到：我们在帮助发布成功的产品。这就是数据分析的核心价值主张，对吧？所以，如果我们希望通过数据分析来助力成功产品的推出，就意味着我们需要更好地理解产品开发，从而更好地运用分析。对吧？我看到的差距在于，大多数其他领域，比如会计、财务，无论你想到哪个领域，背后都拥有非常标准化的理论体系，经过几十年的时间打磨得非常成熟。但对于产品分析而言，这种体系并不存在，它只零散存在于少数几家公司员工的大脑里。所以我的动力就是：我们现在该如何整理出一套能涵盖所有这些知识的行动指南？
Original English >> Right. So the question I was wrestling with was what if they fire everyone uh in in analytics, right? Will the company actually lose anything or will they continue to continue to sustain? So wrestling with this question for a few months I realized that oh we are actually like helping launch successful products. That's what analytics like core value prop is right. So if we are to launch successful products through analytics then it means we need to understand product development better so we can apply analytics better. Right? the the gap I saw was most of the other domains like accounting, finance, whatever domain you think about had very standardized like theory behind them right over a period of time over decades they've all been like kind of fine-tuned a lot but for product analytics that did not exist it was just in the kind of minds of people at few places right so um my motivation was okay how do we like now like kind of uh have a playbook that that kind captures all his knowledge
>> 这本书之所以存在，是因为我们的无知。我们当时不知道这有多难。如果早知道有多难，我想这本书可能永远都不会开工。如果你有合理的预期，它是可以完成的，但过程极其艰难。我觉得这几乎就像一次创业，对吧？你带着无知一头扎进去，但在过程中你努力去创造出令人惊叹的东西。在我的职业生涯中，我在亚马逊开始偏向增长方向。当时我是构建模型的经济学人，但我以为作为数据人员，我的价值在于我能解决困难的事情、能做复杂的建模。后来到了 Facebook，这种想法就被打破了。我记得我的第一份工作是建一个逻辑回归模型或随机森林模型，而我的经理对我的缓慢进度很不满意，尽管当时我已经比亚马逊时期快得多了。他列了几个数据点，写了一篇简报发出来，抄送了所有的 VP，事情就解决了。我才意识到：数据的价值在于做出更好的决策，而不是做复杂的难事。在写书写到一半时，我意识到，我是通过与人交谈、阅读各种笔记、参加 Facebook 的数据分析训练营，或者通过不断犯错才明白这一点的，市面上没有任何一本书能让我系统学到这些知识。
Original English >> and the reason this book exists is because of our ignorance. We didn't know how hard it is knowing how hard it is. I think the book would never get started. If you have the right expectation can be done, but it's very hard. I feel like it's almost like a startup, right? You go in there with ignorance, but along the way you try to do something amazing. Uh yeah, so I gravitated towards growth in my career at Amazon. I was economist building models but I was thinking like my value as a data person is because I can do hard things. I can do modeling. Then Facebook uh that got thrown away. I remember like my first job was I build a logistic regression model or rental force model and uh my manager now wasn't happy about how slow I was even though I was like much faster than I was Amazon. He put a couple data points and wrote a note, published it, tagged all the VPs and uh that was it. And I realized, okay, the value of data is to make better decisions. It's not about doing hard things. But uh like halfway writing the book, I realized, okay, I knew this by like talking with people, by looking at different notes, by for example taking the Facebook analytics boot camp or by making mistakes. there was no book out there that I can learn all this information from.
### 产品增长的常见误区与真正的 PLG 飞轮 >> 那么，大多数人或公司对产品增长存在哪些误解呢？
Original English >> So what do most people or companies get wrong about product growth?
>> 这是一个很好的问题。我认为很多人在把产品驱动增长（PLG）仅仅看作自然有机增长时就搞错了。他们过于依赖口碑，而不投入市场营销，甚至不为销售团队筛选优质线索。我见过有些人觉得“我们的产品在增长，我们就不需要额外发力”，但实际上我认为真正的 PLG 是一个飞轮：基本上，你从早期信任者、早期采用者那里获得这种口碑传播，然后你需要市场营销来放大这种影响力，触达那些原本无法自然发现你的人群；接着，所有这些成为你拥护者的优质线索，你应该转交给销售团队，进而转化为企业级大单。另一方面，这既证明了你的产品具备企业级服务能力，同时也是一个能极大推动 PLG 漏斗的优秀案例，因为大家对你的产品产生了多重信任。所以，我认为这大概是我见过的大家最常犯的一个重大错误。
Original English >> Um that's a great question. Um I think a lot of people get it wrong when they saw PLG just organic growth. They just rely too much on word of mouth without investing in marketing or even picking up the good leads uh for for sales. I've seen people who you know like okay our product is growing like we're just going to not going to grow but actually I think the real PLG is like a flywheel so basically you kind of create this kind of word math from the early believers early adopters and then you actually need the marketing to actually to help to amplify that impact to get to the people who wouldn't have discovered uh you like organically and then all and all these kind of leads like good leads who become your champion and then you should send them to sales and then they become enterprise deal And on the other side they actually one is it proves you know your your product is inter is enterprise ready and on the other side also a great story to actually boost your PLG u funnel as well because people have more multiple trust in your product. Uh so I think that's probably one of the biggest mistake I've been seeing people.
### 衡量产品市场匹配度（PMF）的核心指标 >> 在进入增长阶段之前，你怎么知道自己是否达到了产品市场匹配（Product Market Fit）？能否帮我们从量化的角度来定义它？
Original English >> How do you know if we have product market fit you know even before the growth stage and can you help us define it more quantitatively as well?
>> 如何进行量化衡量？我认为，正如我们常说的，**留存率（retention）才是王道**。我们认为那是衡量产品市场匹配最强有力的指标，对吧？我刚才还跟 Julie 聊到，我以前在 Meta 负责给新加入数据分析团队的人做训练营，我负责讲留存课程。我当时常用的一张幻灯片就是强调留存的重要性，上面有三句引言：一句来自 Julie，另一句［清嗓子］来自 Alex Schultz，还有一句来自施莱普弗（Schrep）。Meta 的所有高层都极其看重留存，对吧？没有人会在不看留存数据的情况下审批通过任何产品。所以留存确实是衡量它的最强指标。
Original English >> Um how do you measure quantitatively? I think um like again retention is the king is how we say we think uh that's the strongest indicator for product marketing right I was telling actually Julie earlier like you know I I used to do this uh boot camp or analytics camp at meta for anyone new in analytics and I used to do the retention class and one of the slide I used to have was around emphasizing the importance of retention and one of and I had three quotes one was from Julie The other one [clears throat] is from Alex Schles and then like from Shrek. So you know all the leaders at Meta, they they deeply care about retention, right? Like no one is going to like sign off on any product without looking at retention. So that's really the strongest indicator for retention.
### A/B 测试的核心：覆盖率与基础设施 >> 好的，Wisy，你是实验领域的行家。能否给我们一些建议，公司应该何时开始做实验？以及，怎样才算是一个健康的实验规模？
Original English >> Okay, Wisy, you are the king of experimentation. you gave us some advice on when should companies start experimenting and um how like what what's like a healthy amount that is
>> 实际上，我认为**覆盖率（coverage）**是一个比实验数量更重要的指标。原因在于，如果你能对100%的新功能进行实验覆盖，你就无法进行挑选（cherry-pick）。做实验的危险之处在于人们会择优挑拣，人们只想证实自己原本的想法，这会演变成一种文化问题。他们只会把自己最有希望的候选方案拿去做实验，但在这种情况下，实验就无法创造任何价值。因为如果你预期它是正向的，实验证实了它是正向的，实验并没有产生新价值。真正的价值在于：你预期它是正向的，结果却发现实际上是负向的。这时你才真正需要更新心智模型，或者调整你的产品路线图。所以我确实认为覆盖率非常重要。在 Facebook，我们几乎能做到100%的覆盖，特别是在那些重要的重大功能上，这并不难做到；但我意识到大多数公司并没有合适的基础设施。虽然我已经离开了公司，但我还是想替 Statsig 打个广告［笑声］。技术上的核心洞察在于：你需要将功能开关（feature flags）和实验作为一个统一的系统，让 feature flag 和实验成为同一个对象。这样每一个使用 feature flag 的功能，你都能免费获得一次实验。如果拥有这种架构，你就能做到100%的覆盖。但这套系统确实是起步容易，规模化扩展很难。
Original English >> I actually consider coverage to be a more important metric than number of experiments and the reason is if you can cover 100% of your new features you cannot cherrapic the danger in doing experimentation is uh people cherrapic people only want to confirm theirel itself and this becomes a culture issue. So they only put up their best candidate to experiment and experimentation creates no value in that case because like if uh you expect this to be positive, experimentation confirm this is positive. Experimentation doesn't create any value. The value is you expect this to be positive and realize it's actually negative. Then you actually need to think update your mental model or update your product road map. uh so I do consider coverage to be important and at Facebook we can almost do 100% coverage especially on the important big features that is not hard to do but I realize most company do not have the right uh infrastructure I I'm going to turn this into a static pitch [laughter] even though I left the company so the the the technical insight is you need to have feature flags and experimentation as one system so feature flags and experimentation is the same object. The every feature you use a feature flag, you get an experimentation for free. If you have this setup, you can do 100% coverage. But the system is actually uh easy to start, hard to scale.
### 数据叙事与图表设计的力量 >> Wise，能不能跟我们多聊聊数据叙事（data storytelling）？你在外面做了很多内容，也在积极布道，在这方面非常高效。作为数据从业人员，你如何看待叙事这项技能？
Original English >> Wise, why don't you tell us a little bit more about data storytelling? You know, I think you are out there, you make a lot of content, you you evangelize, you're very effective at that. How do you think about storytelling as a skill for people who work in data?
>> 我认为这里有两件事：作为数据从业者的叙事能力，以及数据本身的叙事，这两者并不完全相同。但我前任经理的一个观点，我也非常赞同：**数据人员最被低估或最欠缺的两项技能，就是讲故事和做图表**。一张有力的图表能直接展现所有需要的信息，非常引人入胜。而讲故事的能力在于——我都记不清参加过多少次会议，如果我放一张表格或放几张图表，哪怕我能做出信息量非常丰富的图表，参会者走出会议室大概五分钟后，如果问他们是否记得我的核心观点，他们完全记不得。人类大脑对单纯的事实记忆并不深刻。但只要你把数据融入故事中，去讲述一个故事，效果就完全不同。我发现这招对设计师尤其有效。每次我和设计师交流时，我总是倾向于把用户旅程截图放出来，这立刻就变成了设计师能感同身受的痛点。如果我只说“转化率只有12%，太低了”，他们毫无感觉；但如果我说“你看，这个步骤坏掉了，所以我认为转化率才会这么低”，他们会立刻着手修复。因此，故事能够激发共鸣，激发同理心，并促成行动。
Original English So I think there are two things like storytelling as a data person and data storytelling they're not always the same but uh my opinion actually I heard this opinion from my uh previous manager and I strongly agree that 's the two most underappreciated or underdeveloped skills of a data person is storytelling and making charts. uh the like a strong charts just tells you all the things you need to know and it's very compelling and storytelling is this like I I don't remember how many meetings I went into and uh if I put a table or if I put charts even though I can make very informative charts uh after like five five minutes probably after people walk off out of the meeting and ask them you know do you remember my points they don't uh but I think sometimes into the human brain we just don't remember the facts that that well. But once you can do it behind a story, you can actually tell a story. Uh I found this to be super effective on designers actually. Whenever I talk to a designer, I always kind of try to screenshot the user journey and then it becames a pain uh for the for the designer. If I just say you know uh this conversion rate is 12% is too low. They don't feel anything. But if I say oh this stat is broken that's why the conversion I believe the conversion rate is so is so low he will fix it immediately. So I think story drives resonance drives empathy and drives action.
>> 能否多谈谈图表？什么是真正优秀的图表？
Original English >> You you said more can you say more about charts and what is a really good chart?
>> 我认为一张真正好的图表只表达一个观点，且仅此一个观点。很多数据人员常犯的错误，就是试图在图表上塞进过量的信息。我们试图做出一张极其复杂的图表，同时表达十个故事。但这其实关乎促成行动：想想你希望推动什么样的行动，或者你想给出什么建议，然后就只做一张图表。如果你有三个故事、三个数据点或三个结论，就做三张图表，表达清楚并加上注释。是的，我认为**观点鲜明的图表才是好图表**。这就像是你清楚自己的故事，然后找到最能体现你想表达观点的图表。
Original English >> I think a really good chart uh says one point and one point only. Like I see a mistake a lot of data folks might make is we try to overload information on charts. We try to make a chart that is so sophisticated. It tells 10 story at the same time. But just uh this ties to the action part, right? Think about what kind of action you want to inform or you want to recommend and just have one chart. If you have start three stories or three data points or conclusions, make three charts and make it very clear and annotate. Yeah. So I think the strongly opinion chart is a good chart. It's almost like you know your story and then find a chart that's like the best representation of the point you're trying to make.
>> 确实。你刚才说到这点时我就想起来，作为一名数据科学家，表达明确的主观观点曾经让我觉得很不适应。
Original English >> Yeah. When the minute you said that I remember like uh it it actually took it was not comfortable for me as a data scientist to have opinions
>> 但我认为一个优秀的数据人应该有自己的观点。
Original English >> but I think a good data person should have opinions.
### AI 时代的非结构化数据与技能转型 >> 好的，这是一个面向你们所有人的问题。我们现在身处 AI 时代，技术正在改变一切，无论变好还是变坏。我很好奇，对于团队将如何使用 AI，以及这将如何改变人们制定决策或使用数据的方式，你们最大的预测是什么？
Original English >> Okay. This is a question for all of you because of course we're in the AI era and that's changing everything. Uh maybe for good or for bad. I'm very curious to hear about what do you think will change like what are your biggest predictions for how teams will use AI and how that'll change the way that people make decisions or use data
>> 但对现场的听众来说，我认为最相关的点是：“数据”的定义其实很奇特。比如文本也是数据，对吧？但当我们想到数据时，脑子里往往只想到数字，可数据并不仅是数字，数据是一切，对吧？AI 让我们能够去分析和利用那些非定量的数据。这一点变得越来越重要。为什么呢？因为现在大家构建产品的速度快得多。不仅是实验——实验需要很大的样本量；那留存呢？如果你想获取月留存，你需要等整整一个月才能拿到月留存，而且那只是一个数据点。所以我们需要有办法去分析非定量数据并提炼出有价值的洞察。我认为我们现在确实能做到了。所以对于数据科学家的角色：不要把我们当成单纯的数字或数学科学家，我们是真正意义上的“数据”科学家。
Original English >> but for this audience I think the most relevant thing is the definition of data is very strange like text is data right but when we think about data we think about a number but data is not a number data is everything right so AI just enabled us to analyze or use that non quantitative data. Uh and it's more important. Why is that? Because people are building things much faster now. We can not not only experiments, experiments takes a lot of sample size. But what about retention? Retention if you want to get monthly retention, you need a month to get the monthly retention and then you just get one data point. So you need we need to have ways to analyze non-quantitative data and drive useful insights. And I think we can do that actually. So the role of data scientists do not treat us as a number or math scientist. We are like actual data scientist.
>> 第一点，我认为现在所有的数据从业者都有潜力成为真正的构建者。［笑声］谢谢。因为坦白讲，在我看来，数据团队往往是工程团队之外技术功底最强的人。因为我们甚至懂 SQL，对吧？里面有逻辑，你理解 for 循环、while 循环，理解条件语句，代码基本上也就是这些东西，对吧？当然工程师懂 HTML、CSS，他们知道如何构建更复杂花哨的东西。但现在我们有了 AI。所以在我看来，因为数据团队具备扎实的基础，既深入理解数据，又极度理解业务，同时我们也是与所有利益相关者沟通的核心枢纽，我们实际上处于转型为构建者的绝佳位置。这也是我目前在公司尝试和践行的事情。我最近刚上线了一个功能到生产环境，我意识到：天哪，当看到大家真的在持续使用你做出来的功能时，原来是这种感觉；成为一名工程师原来是这种体验。这当然不容易，但绝非不可能。所以我非常鼓励在座的各位真正去动手尝试，去尝试以前从未做过的事情。不仅在数据分析上，在其他方面也是如此。为你的团队构建一个数据驱动的功能。目前我正在尝试将所有统计指标集成到我们的产品中，完全由我一个人来构建这个功能。我不再只是做个原型说“这是 p 值”或者“页面应该长成这样”，而是直接把它做出来。我真的认为 AI 让这一切变得非常可行。那么问题就变成了：我们究竟该如何让大家明白数据科学其实非常重要？
Original English >> The first one is I think all the data people right now have the potential to become a real builder. [laughter] Oh thank you. Uh because to be honest in my opinion um uh like data data people are always the you know the probably the most technical folks outside of engineering team. So because we we understand even SQL right there are some logic you understand for loop while loop understand conditions that's all the codes about basically right uh and of course engineer knows some HTML CSS they know how to build something fancier uh but now we have AI right so I think to me uh because data team like they have this kind of very solid foundation understand the data understand the business very well also we are also kind of the center of kind of the communication center of talking with all the stakeholders we're actually in a perfect place to become a builder. Uh that's something I 'm trying actually experimenting at my current company. Uh I recently just launched a feature into the pro into production and I realized oh my god that's how it feels like when you actually keep people on using your feature. That's how it looks like to become engineer. It's it's of course it's not that easy but it's not impossible. So I think I really encourage everybody here to really try to get your hands dirty. Try try something you never done before. Not only on an analytics but also on something else. Build a data feature for your team. Right now I'm trying to implement all the statistics into our product as well. I just building a feature just by myself. I'm not just you know doing a mock and tell okay this is p value or this is how the company was supposed to look. I just build it. So I really think AI makes it very possible. So the question is uh how do we actually make people understand data science is actually very important.
### 从间接建议到直接构建 >> 是的，我们该如何讲好这个故事？
Original English >> Yeah. How do we tell that story?
>> 这是个好问题。你想先聊聊讲故事吗？
Original English >> That's a great question. Do you want to start with the story telling?
>> ［笑声］
Original English >> [laughter]
>> 其实我不太适合回答这个问题，因为我总是告诉数据科学家，不要再当个单纯的数据科学家了［笑声］，而是去成为一名构建者。确实如此。因为我们平时所做的事情其实相当间接，对吧？我认为我们在信息复杂的大型组织里蓬勃发展。普通人往往看不懂数据，我们具备精通数据的专长，能够提出更好的建议，从而间接推动行动。但我不喜欢这种方式，我想直接把行动变成现实。所以我总是告诉数据科学家要成为构建者，而不是反过来。我不会在这个数据壁垒里对大家宣传数据科学家有多重要。我希望所有数据科学家都能直接去实施变革，而不必依赖工程师来实现改变。
Original English >> I'm actually not a good person for this because I always tell data scientists to stop being a data scientist [laughter] and to be a builder actually. Yeah. Because like what we do is quite indirect, right? And uh I think we strive in large organizations with complex information. So we like people just cannot understand data. we have a specialty of understanding it well and we can make better recommendations and indirectly drive actions. I don't like that. I want to just make the action happen. So I always tell data scientists to be builders but not the other way around. I don't tell people that data scientists are important in this data fridge. Uh I I hope all data scientists can directly implement the change. They don't have to rely on engineer to make the change happen.
### 核心竞争力与对初级岗位的冲击 在当前的产品或竞争环境中，你们认为脱颖而出的最独特或最好的技能是什么？
Original English How do you guys think about the the unique or the best skill that you think stand out among this like product or competitive space?
>> 我对普通人群的答案与对所有数据科学家的答案是一样的：**自主能动性（agency）和品味（taste）**。当你拥有自主能动性时，你就能做很多别人根本没想过去做、或者一直无法执行的新事情；而品味，最终是让你变得独一无二、并做出更好决策的关键。
Original English >> I think the the the my answer for the general population is the same for all data scientists is agency and taste. When you have agency, you can do a lot of new things that other people are not think about doing or just uh hasn't been able to execute. and taste is ultimately what uh makes you unique and makes you you know having better decisions. So
>> 我可能还会补充一点，那就是**好奇心**。我认为那其实是一切背后的乐趣所在。
Original English >> I probably would add one thing is probably curiosity. I think that's actually the joy behind everything.
>> 我认为显而易见的是，它对初级岗位造成了不成比例的冲击。举个例子，Statsig 之前发布了一个初级数据科学家的职位，这在现在其实非常罕见。大家已经不再招初级数据科学家了。我想我们在一天之内就收到了3000份简历。所以我认为这种影响确实存在，且不成比例地冲击了初级岗位。但我之前邀请 Hobby 参加过另一个研讨会，他对类似问题的回答是：20年前根本没有数据科学家。数据科学家是一个发明出来的头衔，它不像农民，对吧？它甚至不像清洁工，它是一个人造的角色。所以我确实认为我们角色的内涵将会改变。因此，无论你是资深人员还是初级人员，都要专注于你正在解决的问题。对于资深人员，我们招聘你可能是因为你对业务运作拥有更好的心智模型，因而拥有更好的判断力；而初级人员，或许你更加多面手：你既能写代码，又能做分析，并且受过统计学训练，所以你的分析非常严谨。因此，我认为如果你拆解出什么是真正有价值的特质——尤其是当技能本身也变得越来越便宜的时候——你或许会有更好的机会找到工作。但我对你问题的后半部分有些疑问：如果你没有看到业务成果的正向增长，你为什么到底需要智能体（agent）？我认为引入 agent 的原因，要么是能节省成本、做得更快，要么是能做以前做不到的新事情。如果你没有解锁任何新能力，并且没有任何 KPI 表现——甚至连成本都没有显示出改善——那就不要盲目使用 agent。
Original English >> I think evidentally it it does disproportionately impact junior roles. As an example, Stac put up a position for junior uh data scientist which was actually quite rare. people do do not hire junior data scientist anymore. We got I think 3,000 applications within a day. So I think there was an impact and uh it disproportionately impact uh junior roles but actually I invited Hobby to another panel and H's answer to a similar question was uh there was no data scientist 20 years ago. Data scientist is a invented job title. It's not like a farmer, right? It's not like a toilet cleaner even it's a inventive role. So I I do think the meaning of our role is gonna change. Uh so whether you are a senior or junior person uh go for what problem you are solving for. Maybe the senior person we are hiring for you have a better mental model about how the business should work. So you have better judgment. Junior person maybe you are more versatile. You can code and you can do analysis and you have a statistical training. So analysis actually is rigorous. So I think if you break down like what is valuable, what trait is valuable especially because skills are getting cheaper as well. So then maybe you have a better chance of landing a job. But the second half your question I was wondering like why do you need that agent at all? If you don't see the business outcome, positively increase. I think the reason you want a agent is either you can save cost, do it faster, or you can do new things that you could n't do before. If you didn't unlock any new things and there's no KPI, like even your cost doesn't show like there's no improvement, don't use agents.
### 结语 >> 好的，非常感谢。让我们把热烈的掌声送给我们的……感谢大家今晚的参与。
Original English >> All right. Thank you. Um, let's give a big round of applause to our Thank you everyone for joining us tonight.