介绍 (Introduction)

In an increasingly technological world, data scientist and analyst roles have emerged, with responsibilities ranging from optimizing Yelp ratings to filtering Amazon recommendations and designing Facebook features. But what exactly do data scientists do? The parameters of this role are rarely strictly defined, but data-oriented work has become imperative to the success of all technology companies.

在一个技术日新月异的世界中，数据科学家和分析人员的角色已经出现，职责范围从优化Yelp等级到过滤Amazon建议和设计Facebook功能。 但是数据科学家到底在做什么？ 很少严格定义此角色的参数，但是面向数据的工作已成为所有技术公司成功的当务之急。

The full job description depends strongly on the type of company. You may find yourself with an unfamiliar set of new tasks when switching from a start-up to a mid-size company, or to FAANG (Facebook, Amazon, Apple, Netflix, Google).

完整的职位描述在很大程度上取决于公司的类型。 从初创公司转到中型公司或FAANG(Facebook，Amazon，Apple，Netflix，Google)时，您可能会遇到一系列陌生的新任务。

Interested in learning more about FAANG companies? Read these company guides about Facebook, Amazon, Apple, Netflix, and Google!

有兴趣了解更多关于FAANG公司的信息吗？ 阅读有关Facebook ， Amazon ， Apple ， Netflix和Google的这些公司指南！

But what type of company fits best for you? The answer lies in realizing the major differences between these types of companies- the type of work, the expected experience, the prioritized skills- all of which contribute to a more holistic understanding of precisely what the role entails.

但是哪种类型的公司最适合您？ 答案在于实现这些公司类型之间的主要差异(工作类型，预期经验，优先技能)，所有这些都有助于更全面地了解角色的确切含义。

创业公司 (Startup Companies)

Startup companies describe those emerging in a fast-paced business world, rapidly developing an innovative product or service. The U.S. Small Business Administration officially describes a startup as a “business that is typically technology oriented and has high growth potential”; high growth potential referring to employees, revenue, or market. This type of company is unique in mainly two aspects: diversity of work and a low head count.

初创公司描述了那些在快速发展的商业世界中新兴，Swift开发创新产品或服务的公司。 美国小企业管理局正式将初创公司描述为“通常以技术为导向并具有高增长潜力的企业” ； 涉及员工，收入或市场的高增长潜力。 这类公司在两个方面很独特： 工作多样化和人数少。

工作的多样性 (Diversity of Work)

A data science role at a startup company involves a little of everything. It requires a jack of all trades with knowledge in data engineering, machine learning, analytics, data visualization, and work that may not be traditionally characterized as ‘data science’.

在一家初创公司中，数据科学角色几乎不涉及任何事情。 它需要具备所有知识，包括数据工程，机器学习，分析，数据可视化以及传统上不能称为“数据科学”的工作。

You might be expected to dial into marketing meetings, or work closely with engineers to deploy models and build out engineering pipelines. The biggest benefit from working at a startup is the acquisition and development of diverse skills, which is rarely seen in larger companies. As a data scientist at a startup, expect to be tasked with problems where you have to “figure it out”. This results in lots of self-learning, self-pacing, ownership and independence.

您可能需要参加营销会议，或与工程师紧密合作以部署模型并建立工程管道。 在初创公司工作的最大好处是获得和发展了各种技能 ，这在大型公司中很少见。 作为初创公司的数据科学家，期望承担一些必须“弄清楚”的问题。 这导致了许多自学，自定进度，所有权和独立性。

低人数 (Low Head Count)

Because startups have fewer employees, it would be much easier to receive a promotion as the company grows. However, a low headcount is a double-edged sword. A smaller company with less people usually has less funding, which on average means a lower salary when compared to larger companies. Thus, a common career path is to start at a larger company, gain experience and receive a higher salary, then transition to a startup for a more diverse experience and career advancement.

由于初创公司的员工人数较少，因此随着公司的成长， 获得晋升会容易得多。 但是，人数少是一把双刃剑。 人数较少的小型公司通常资金较少，与大型公司相比，这意味着平均工资较低 。 因此，一条常见的职业道路是从一家较大的公司开始，获得经验并获得更高的薪水，然后过渡到一家初创公司，以获得更多样化的经验和职业发展。

Although the career ladder at a startup may be easier to climb, you won’t have as much work-life balance. The faster pace of a startup results in a constantly-changing and dynamic environment- and while becoming a director could be possible within a few years, the skills necessary to build a successful business require much more time and perseverance to hone.

尽管初创公司的职业阶梯可能更容易攀登，但您将没有太多的工作与生活平衡。 初创公司更快的步伐会导致不断变化和动态的环境，虽然可能在几年内成为董事，但建立成功企业所需的技能需要花费更多的时间和毅力来磨练。

FAANG公司 (FAANG Companies)

FAANG is an acronym that represents the top five performing technology companies: Facebook, Amazon, Apple, Netflix, and Google. These tech giants differ from startups in four main areas: efficiency, processes, responsibilities, and career trajectory.

FAANG是首字母缩写词，代表表现最佳的五家技术公司：Facebook，亚马逊，苹果，Netflix和Google。 这些技术巨头在四个主要方面与初创公司不同： 效率，流程，责任和职业轨迹 。

Note: We refer to data scientists at FAANG companies exclusively in this section, however the described role also represents the data science position at other large tech companies with a high employee count.

注意：在本节中，我们仅指FAANG公司的数据科学家，但是所描述的角色也代表了在其他拥有大量员工的大型高科技公司中的数据科学职位。

效率 (Efficiency)

Global technology superpowers have tens of thousands of employees, all of whom perform their own unique tasks. Work output is measured precisely, and members of teams are placed in a hierarchy. In this sense, work life is imbued with order- tasks are well-defined, employees report to one boss, and employee success is measured. Compared to the more fluid nature of a startup position, this role is more straightforward to manage and understand.

全球技术超级大国拥有成千上万的员工，他们全部执行自己独特的任务。 精确测量工作输出，并将团队成员置于层次结构中。 从这个意义上讲，工作生活充满了订单，任务被明确定义，员工向一位老板汇报工作，衡量员工的成功。 与起初职位的流动性相比，此角色更易于管理和理解。

处理 (Process)

In an experienced and well-managed company, a transition from academia or previous employment to this role will be seamless. Bootcamps are a common resource that prepare future employees with the necessary skills for their role across several divisions.

在一家经验丰富且管理完善的公司中，从学术界或以前的工作到此职位的过渡将是无缝的。 训练营是一种通用资源，可以使未来的员工具备跨部门的必要技能。

职责范围 (Responsibilities)

The average work experience will revolve around analytics and creating dashboards. Whether it is analyzing cohesive company performance or the success of a certain feature, the data analytics job will be pretty straightforward.

平均工作经验将围绕分析和创建仪表板。 无论是分析具有凝聚力的公司绩效还是某个功能是否成功，数据分析工作都将非常简单。

职业轨迹 (Career Trajectory)

As mentioned earlier, it is generally harder to climb the career ladder at a FAANG company. However, it may be easier to make money as an individual contractor (IC); the role generally entails a deep dive into both optimizing and producing products. The career ladder is wildly different than one at a start-up, climbing to a director position can take decades of commitment.

如前所述，通常很难在FAANG公司攀登职业阶梯。 但是，作为独立承包商(IC)赚钱可能更容易； 该角色通常需要深入研究优化和生产产品 。 职业阶梯与刚起步的职业阶梯截然不同，晋升为董事职位可能需要数十年的努力 。

For example, a typical career ladder at Amazon may go from Business Analyst to Business Intelligence Engineer to Data Scientist to Research Scientist, with each subsequent role having more pay. Each role also has four ‘stages’: levels I, II, III (Senior), IV (Principal). As seen in the tiered hierarchies within these companies, there is a clear-cut path to promotion- but also many more stages to ‘complete’ compared to a similar promotion at a startup.

例如，在亚马逊，典型的职业阶梯可能是从业务分析师到商业智能工程师再到数据科学家再到研究科学家，而每个后续职位的薪水都更高。 每个角色也有四个“阶段”：I，II，III(高级)，IV(负责人)。 从这些公司的层次结构中可以看出，晋升有一条明确的途径，但与初创企业进行类似的晋升相比，还有更多的“完成”阶段。

中型公司 (Mid-size Companies)

Although exact definitions vary across industry and countries, according to the Organization for Economic Cooperation and Development, a mid-size business generally has between 50 and 250 employees. This type of company can be seen as the middle ground between a start-up and a FAANG company.

尽管确切定义在行业和国家/地区之间有所不同，但根据经济合作与发展组织(OECD)的数据，中型企业通常拥有50至250名员工 。 这类公司可以看作是初创公司与FAANG公司之间的中间地带。

As the rapid growth phase of a startup plateaus out and the company begins to feel the pressure of the market and competitors, mid-size companies experience what is fittingly described as, “growing pains.” On the employees’ side, a sense of balance is achieved between the freedom of startups and the structure of FAANG. In this sense, while the data scientist role is designed to adapt to different needs, there is simultaneously a clear set of responsibilities to fulfill.

随着初创公司的快速成长阶段趋于平稳，公司开始感受到市场和竞争对手的压力，中型公司将经历被恰当地描述为“成长中的痛苦” 。 在员工方面，一颗平常心是初创企业的自由和舫的结构之间实现。 从这个意义上讲，尽管数据科学家的角色旨在适应不同的需求，但同时要履行一系列明确的职责。

Finally, while the negatives are balanced on an even ground, the benefits are split as well. The average salary for a data scientist at a mid-size company will be more than at a startup, but less than at a FAANG company. The opportunities for promotion are also in between that of a startup and a FAANG. Although being a major contributor to the company is not guaranteed; with patience and perseverance, it’s possible to scale a team and bring great value to the company.

最后，虽然负面因素在一个平衡的基础上得到平衡，但收益也各不相同。 中型公司的数据科学家的平均薪水将高于初创公司，但低于FAANG的公司。 晋升机会也介于初创公司和FAANG之间。 虽然不能保证成为公司的主要贡献者； 只要有耐心和毅力，就可以扩大团队规模并为公司带来巨大的价值。

摘要 (Summary)

You may be asking, “What size company is the best for me?”

您可能会问：“什么规模的公司最适合我？”

A person’s ideal company size largely depends on that individual’s personal goals and priorities- is it payment, promotions, or diverse experiences? Or perhaps a mixture of all? Nonetheless, given that the data science revolution across the globe is continually growing, there is one question that remains: “How do I find a data science job?”

一个人理想的公司规模在很大程度上取决于该人的个人目标和优先事项-是付款，晋升还是多样化的经历？ 还是所有这些的混合体？ 尽管如此，鉴于全球数据科学革命正在不断发展，仍然存在一个问题：“我如何找到数据科学工作？”

The answer is: Check out Interview Query!

答案是： 签出面试查询！

Originally published at https://www.interviewquery.com on July 31, 2020.

最初于 2020年7月31日 发布在 https://www.interviewquery.com 。