相当一名科学家的规划

by Carl Dawson

通过卡尔道森

如何成为一名自由数据科学家 (How to become a freelance Data Scientist)

作为一名数据科学自由职业者，如何营销您的服务，寻找客户并收取更多费用 (How to market your services, find clients, and charge more as a data science freelancer)

While it’s true that the best data science is done by those who know their organization very well, there’s a lot about data science that lends itself well to consulting style engagements. I’ve worked as a freelancer in data science (and analytics more generally) for the better part of a decade, and in this post I’ll be showing you how to freelance using your data science skills.

确实，最好的数据科学是由非常了解其组织的人完成的，但是很多数据科学都非常适合咨询风格参与。在过去十年的大部分时间里，我一直是数据科学(和更广泛的分析)领域的自由职业者，在这篇文章中，我将向您展示如何利用数据科学技能进行自由职业。

唯一的要求 (The only requirement)

There’s really only one thing you need to do in order to become a freelance data scientist — you need to get clients to pay you money to do data science work. That’s it.

要成为一名自由数据科学家，您实际上只需要做一件事-您需要让客户付钱以从事数据科学工作。而已。

Other posts on this topic will discuss things like getting a business license, saving up 6 months of expenses. They’ll list all the skills you should be proficient in before seeking your first client. But that’s how you do things the wrong way around. You could have all the skills in the world, a business license, and a nest egg. But without paying clients you’re not a freelancer, you’re just taking a career break.

关于此主题的其他帖子将讨论诸如获得营业执照，节省6个月的费用之类的事情。在列出您的第一个客户之前，他们将列出您应该精通的所有技能。但这就是您以错误的方式做事的方式。您可能拥有世界上所有的技能，营业执照和储备金。但是，如果不付钱给客户，您就不是自由职业者，您只是在休假。

With that said, let’s get started on the important stuff.

话虽如此，让我们开始学习重要的东西。

如何获得客户 (How to get clients)

There’s an adage in the freelance world that goes something like this — anybody can get a first client but only a true business person can get a second. What this means is that anybody can find a client through their network. They can pester friends, cousins, successful uncles. They can contact previous employers. They can talk to enough people that they’ll eventually stumble into some work.

在自由职业者世界中有一句格言是这样的：任何人都能获得第一位客户，但只有真正的商人才能获得第二位。这意味着任何人都可以通过他们的网络找到客户。他们会缠扰朋友，堂兄，成功的叔叔。他们可以联系以前的雇主。他们可以与足够多的人交谈，以至于他们最终会涉足某些工作。

Leveraging your network like this is both a blessing and a curse. When you land that first client the day after starting your freelancing experiment, you’ll think that it will always be that easy. You’ll rely on your network to get you the next project. And you’ll cruise through your relationship with your first client without ever putting in the sales and marketing work needed to ensure that there’s another one waiting for you when you’re finished. Only to find that the network has dried up.

像这样利用您的网络既是福也是祸。当您开始自由职业实验的第二天找到第一位客户时，您会认为这总是那么容易。您将依靠您的网络来获得下一个项目。而且，您将无需再进行必要的销售和营销工作即可确保与第一个客户的关系畅通无阻，以确保完成后还有另一个人在等您。只是发现网络干dried了。

Let me be clear. You should tell everybody you know that you’re going freelance. A network never hurts. But if you want to have a serious go at being a freelance data scientist, you absolutely can’t rely on your network to find you projects every time you’re out of work.

让我清楚一点。您应该告诉所有人，您知道自己将成为自由职业者。网络永无伤害。但是，如果您想认真地成为一名自由数据科学家，则绝对不能在每次失业时都依靠网络来找到您的项目。

Data Science is different to most freelance disciplines - very few people need it (unlike hairdressing services, for example), success is objective (unlike logo design), and it’s very high-value work that hasn’t been commoditized yet (unlike basic coding). It’s important to keep these distinctions in mind when planning your marketing and lead generation efforts.

数据科学不同于大多数自由职业学科-很少有人需要它(例如不同于美发服务)，成功是客观的(不同于徽标设计)，并且它是非常高价值的工作，尚未被商品化(不同于基本编码) )。在计划营销和潜在客户生成工作时，请务必牢记这些区别。

认识你的顾客 (Know you customer)

There are usually only a handful of people in every organization that have the power to purchase data science consulting services. They’re the CEO, the CTO, the software engineering manager, and the department head that’s working on a crucial project in their domain (such as a Head of Risk or Head of HR).

每个组织中通常只有少数人有权购买数据科学咨询服务。他们是首席执行官，CTO，软件工程经理和部门负责人，负责各自领域中的关键项目(例如风险主管或人力资源主管)。

These people are your customers. You should get to know them.

这些人是您的客户。您应该了解他们。

If you’ve put up a website describing your services and set up an AdWords campaign hoping that these people will be looking for ‘Freelance Data Scientist’ in Google, you’ve done it wrong. This group of people will never find you that way. What you find if you search for ‘Freelance Data Scientist’ is articles like this one (including another one of mine), articles explaining how to become a freelance data scientist or what the day-to-day life of a freelance data scientist is like.

如果您建立了一个描述您的服务的网站并设置了一个AdWords广告系列，希望这些人在Google中寻找“自由数据科学家”，那么您做错了。这群人永远不会那样找到你。如果您搜索“自由数据科学家”，您会发现像这样的文章(包括我的另一篇)，这些文章解释了如何成为自由数据科学家或自由数据科学家的日常生活是什么样的。

The people in the position to purchase your services don’t start with a desire to purchase, they start with a problem.

能够购买您的服务的人并不是从购买欲望开始，而是从问题开始。

Here’s a case-by-case explanation of what I mean:

这是我的意思的逐案解释：

CEOs are under pressure to stay competitive and profitable. Their problem is finding the best ways to gain an edge in a competitive marketplace. They (usually) don’t care if you know your Adaboost from your Adagrad. They simply need help improving the one or two numbers their job performance is judged by.

首席执行官承受着保持竞争力和盈利的压力。他们的问题是寻找在竞争激烈的市场中获得优势的最佳方法。他们(通常)不在乎您是否从Adagrad了解Adaboost。他们只是需要帮助来提高其工作表现所依据的一两个数字。

CTOs and Software Managers are the people CEOs turn to when they think a business problem needs to be solved through the smart use of technology. CTOs aren’t looking to hire freelance data scientists for the hell of it. No CTO I’ve ever met has had the ‘my budget’s too big’ problem. These are the people who will try to figure things out themselves (with help from their teams). This means that they’ll be searching for specific answers to specific questions. What is the best algorithm for Churn Prediction? Can I use Azure ML with AWS Redshift? That kind of thing.

当首席执行官认为需要通过智能使用技术来解决业务问题时，他们会使用CTO和软件经理。 CTO并不想为此雇用自由数据科学家。我从未见过的CTO遇到过“我的预算太大”的问题。这些人将尝试自己解决问题(在团队的帮助下)。这意味着他们将搜索特定问题的特定答案。搅动预测的最佳算法是什么？我可以将Azure ML与AWS Redshift一起使用吗？那种事

Finally, the departmental managers who run vertical-specific projects are not looking for freelance data scientists because they have no idea what a data scientist is. They need to know how to calculate employee turnover on a pro-rota basis over a group of 10,000+ employees. They need to know how to automate certain sections of their underwriting procedures to reduce the man-hours spent on menial tasks. They need honest advice from an expert in words they can understand.

最后，运行垂直特定项目的部门经理不会寻找自由数据科学家，因为他们不知道什么是数据科学家。他们需要知道如何按比例计算超过10,000名员工的员工流失率。他们需要知道如何使承保程序的某些部分自动化，以减少花在琐事上的工时。他们需要专家以他们能理解的语言向他们提供诚实的建议。

Running ads against the words ‘Freelance Data Scientist’ might be a lot easier, but it’s a lot less effective than helping any of these people with their actual problem.

使用“自由数据科学家”一词来投放广告可能会容易得多，但效果不如帮助任何一个人解决实际问题。

选择一个利基 (Pick a niche)

Data Science is a broad field. Nearly every data scientist worth their salt could put together a rudimentary data warehouse, create a dashboard, implement linear regression from scratch, and train a convolutional neural net. This is what makes the field fun.

数据科学是一个广阔的领域。几乎每位值得其投入的数据科学家都可以建立起一个基本的数据仓库，创建仪表板，从头开始执行线性回归，并训练卷积神经网络。这就是使现场变得有趣的原因。

Unfortunately, diverse skill sets make for indistinguishable businesses.

不幸的是，各种各样的技能使企业难以区分。

In order to reach your customers (listed above) you’ll have to create marketing plans dedicated specifically to their problems.

为了吸引您的客户(上面列出)，您必须创建专门针对他们的问题的营销计划。

Some people think that data science is already a niche. I’d argue otherwise but that semantic distinction isn’t even important here — the more focused you can be, the better your marketing will work.

有人认为数据科学已经是一个利基市场。否则我会争论，但是语义上的区别在这里甚至都不重要-越专注，营销就越有效。

The reaction you want from someone visiting your website is ‘I can’t believe this exists’. You want to come up with an offering so unique and tailored to a specific group of people that their decision to purchase (or contact you at the very least) becomes automatic.

您希望来自访问您网站的人的React是“我不敢相信这个存在”。您想要提供一种如此独特且针对特定人群的产品，以使他们购买(或至少与您联系)的决定会自动发生。

This doesn’t mean that you can’t do anything else for your entire career and that you’ll always be known for just one thing. It means that the people you care about finding you will find you. Once you’re in the door, and once you’ve built up trust, you can work with them on any projects you wish. In fact, if you do the first project well, they’ll probably ask you to do the second, regardless of whether or not it’s in your niche.

这并不意味着您在整个职业生涯中都无法做任何其他事情，并且您将永远只因一件事而闻名。这意味着您关心的人会找到您。一旦进入家门，并建立起信任，就可以与他们合作进行任何您想要的项目。实际上，如果您将第一个项目做得很好，他们可能会要求您执行第二个项目，无论它是否在您的利基市场中。

To give you some examples of niches, here are the ones that I’ve had throughout my career:

为了给您一些利基的例子，以下是我整个职业生涯中遇到的一些问题：

Building data warehouses for data science projects in FMCG businesses为快速消费品行业的数据科学项目建立数据仓库
Building recommendation engines for content websites为内容网站构建推荐引擎
Predicting rare events in retail and e-commerce (purchase, churn, fraud, theft)预测零售和电子商务中的罕见事件(购买，流失，欺诈，盗窃)
Using computer vision in SaaS apps在SaaS应用中使用计算机视觉

Each of these relies on a specific area of data science and a specific industry. For your first niche, you should pick the area of data science you’re most comfortable with and an industry that you care about helping.

这些都依赖于数据科学和特定行业的特定领域。对于您的第一个利基市场，您应该选择最熟悉的数据科学领域以及您关心的行业。

共享营销 (Marketing by sharing)

Once you’ve chosen a niche you need to get to work marketing inside of it. Most people like to learn and most people will try to solve their problems by themselves first. You will go a long way teaching others.

选择利基市场后，您需要在其中进行营销。大多数人喜欢学习，大多数人会首先尝试自己解决问题。您将在教别人方面大有帮助。

I read so many data science blogs that analyze the same Kaggle data sets over and over again. Or worse, they use np.linspace to generate a data set to explain some concept or phenomena. This is not how data science works in business. Articles like this start too late (they don’t describe the data retrieval and cleaning efforts) and stop too early (they don’t describe the business impact of the solution).

我读了很多数据科学博客，这些博客一遍又一遍地分析相同的Kaggle数据集。更糟糕的是，他们使用np.linspace生成数据集来解释某些概念或现象。这不是数据科学在业务中的工作方式。诸如此类的文章起步太晚(它们没有描述数据检索和清理工作)，而结束的时间也太早了(它们没有描述解决方案的业务影响)。

If you’re going to write a blog post to explain a concept in the hope that a potential client will find it, you need to explain in the article how the concept solves their problem.

如果您打算写博客文章来解释一个概念，希望潜在的客户能找到它，则需要在文章中解释该概念如何解决他们的问题。

People seeking data science clients should never write a general exposition of a particular algorithm. Go the extra distance and find a problem area in your niche that you can apply it to. And do this a lot. The more you write about your niche, the more people will equate your name with that specific area, you’ll become the no-brainer hire.

寻求数据科学客户的人们永远不应编写特定算法的一般说明。走得更远，在您的利基市场中找到一个可以应用的问题区域。并且做很多。您撰写的利基信息越多，就会有更多的人将您的名字等同于该特定领域，您将变得轻而易举。

If you build up a large enough body of this kind of work, your SEO game will be on top form and customers will start finding you. Not everybody will convert but some will and they’ll be the people whose problems you know how to solve.

如果您构建了足够大的这种工作主体，那么您的SEO游戏将处于最佳状态，客户将开始找到您。不是每个人都会convert依，但有些人会convert依，他们将成为您知道如何解决问题的人。

建立一个过程 (Build a process)

Now that you’re getting clients, you need to optimize the process of working with them. This is important for two reasons:

现在您正在获得客户，您需要优化与他们合作的过程。这很重要，原因有两个：

It helps the engagement go smoothly and prevents scope-creep它有助于使交接顺利进行并防止范围变大
It allows you to extract and reuse key deliverables它允许您提取和重用关键的可交付成果

Another benefit of having a niche is that you end up seeing the same problems time and time again. You can reuse the same algorithms, explain your solution in the same way, and run the same discovery meetings over and over again. This saves you an incredible amount of time and allows you to focus on the important work in the project.

拥有利基市场的另一个好处是，您一次又一次地遇到相同的问题。您可以重复使用相同的算法，以相同的方式解释您的解决方案，并一次又一次地运行相同的发现会议。这为您节省了不可思议的时间，并使您可以专注于项目中的重要工作。

The process you end up developing will become a selling point. It will be the thing you use to explain how you work in case studies and pitch presentations. So as soon as you can, you should start documenting the stages of every project, getting all of your deliverables in order, and learning how to write case studies based on your process.

您最终开发的过程将成为卖点。这将是您用来解释案例研究和演讲演示的工作方式的东西。因此，您应该尽快开始记录每个项目的阶段，整理好所有可交付成果，并学习如何根据流程编写案例研究。

双倍收费 (Double your rate)

Most freelancers charge too little. They’ll do some algebra and work out how much they made per hour at their old job and use that. Despite lots and lots of advice to the contrary, that still seems to be the way people go.

大多数自由职业者收费太低。他们将做一些代数运算，并计算出他们在旧工作中每小时赚多少并使用它。尽管有很多相反的建议，但这似乎仍然是人们前进的方向。

So, what I’ll say is this — if nobody pushes back on your rate when you submit a proposal, double it next time. And keep doubling it until someone says it’s too much.

因此，我要说的是-如果在提交提案时没有人降低您的费用，则下次再加倍。并不断加倍，直到有人说太多。

It might be easy to write this off as greedy or self-destructive but the fact is, if you are doing very niche work, you’re going to very quickly become an expert at it. Experts get to charge a premium. You should be adequately rewarded for developing processes and deliverables that allow you to do the work quicker than anyone else. Why should you be penalized for being efficient?

将其简化为贪婪或自我毁灭可能很容易，但事实是，如果您做的是非常专业的工作，您将很快成为专家。专家要收取额外费用。开发流程和可交付成果使您比其他任何人都能更快地完成工作，应该得到充分的回报。为什么要因效率高而受到惩罚？

制定退出策略 (Have an exit strategy)

Without a specific plan, relationships with clients go the way of entropy. They say they’ll get back to you when they have a new project, you’ll say thanks for the opportunity.

如果没有特定的计划，与客户的关系就会变成熵。他们说当他们有一个新项目时，他们会尽快与您联系，您会说声谢谢。

The moment that you’ve finished doing great work for a client is the exact right time to ask for referrals and testimonials. When you’re finished, sending an email with a short questionnaire about how the process went and asking for the names and contact details of people with similar issues makes you look like a professional who cares about helping the community.

为客户完成出色工作的那一刻，正是寻求转介和推荐的正确时机。完成后，发送一封电子邮件并附上简短的问卷调查表，以了解该过程的进展，并询问具有类似问题的人员的姓名和联系方式，这使您看起来像是一位乐于帮助社区的专业人员。

The only time this would be a problem was if the work was substandard. So do good work and ask to be referred.

唯一会出现问题的是工作是否不合格。因此，请做好工作并要求转介。

Glowing quotes help with social proof for your marketing materials. And the exit questionnaire allows you to determine what the client would be happy to see in a case study about them.

发光的引号可为您的营销材料提供社交证明。通过退出问卷，您可以确定在案例研究中客户希望看到的内容。

Asking a client to write you a recommendation (without being pushy) gives them the chance to reflect on the good things that happened throughout the project and to end on a high note instead of the pressured, rushed feelings that large-scale deployments can usually leave you with.

要求客户给您一个建议(不要太仓促)，使他们有机会反思整个项目中发生的美好事情，并以高调结束，而不是通常会离开大型部署的压力，仓促的感觉。你和。

谢谢阅读 (Thanks for reading)

If you have any questions about being a freelance data scientist, feel free to email me.

如果您对成为自由数据科学家有任何疑问，请随时给我发送电子邮件。

If you’re interested in getting more specific advice on freelancing as a data scientist, I’m now offering a course.

如果您有兴趣作为数据科学家获得有关自由职业的更多具体建议，那么我现在正在提供一门课程。

Originally published at machinelearningphd.com on March 9, 2019.

最初于2019年3月9日发布在machinelearningphd.com上。

翻译自: https://www.freecodecamp.org/news/how-to-become-a-freelance-data-scientist-7b55ab787df2/