介紹 (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.

在一個技術日新月異的世界中，數據科學家和分析人員的角色已經出現，職責范圍從優化Yelp等級到過濾Amazon建議和設計Facebook功能。 但是數據科學家到底在做什么？ 很少嚴格定義此角色的參數，但是面向數據的工作已成為所有技術公司成功的當務之急。

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).

完整的職位描述在很大程度上取決于公司的類型。 從初創公司轉到中型公司或FAANG(Facebook，Amazon，Apple，Netflix，Google)時，您可能會遇到一系列陌生的新任務。

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

有興趣了解更多關于FAANG公司的信息嗎？ 閱讀有關Facebook ， Amazon ， Apple ， Netflix和Google的這些公司指南！

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.

但是哪種類型的公司最適合您？ 答案在于實現這些公司類型之間的主要差異(工作類型，預期經驗，優先技能)，所有這些都有助于更全面地了解角色的確切含義。

創業公司 (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.

初創公司描述了那些在快速發展的商業世界中新興，Swift開發創新產品或服務的公司。 美國小企業管理局正式將初創公司描述為“通常以技術為導向并具有高增長潛力的企業” ； 涉及員工，收入或市場的高增長潛力。 這類公司在兩個方面很獨特： 工作多樣化和人數少。

工作的多樣性 (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’.

在一家初創公司中，數據科學角色幾乎不涉及任何事情。 它需要具備所有知識，包括數據工程，機器學習，分析，數據可視化以及傳統上不能稱為“數據科學”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.

您可能需要參加營銷會議，或與工程師緊密合作以部署模型并建立工程管道。 在初創公司工作的最大好處是獲得和發展了各種技能 ，這在大型公司中很少見。 作為初創公司的數據科學家，期望承擔一些必須“弄清楚”的問題。 這導致了許多自學，自定進度，所有權和獨立性。

低人數 (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.

由于初創公司的員工人數較少，因此隨著公司的成長， 獲得晉升會容易得多。 但是，人數少是一把雙刃劍。 人數較少的小型公司通常資金較少，與大型公司相比，這意味著平均工資較低 。 因此，一條常見的職業道路是從一家較大的公司開始，獲得經驗并獲得更高的薪水，然后過渡到一家初創公司，以獲得更多樣化的經驗和職業發展。

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.

盡管初創公司的職業階梯可能更容易攀登，但您將沒有太多的工作與生活平衡。 初創公司更快的步伐會導致不斷變化和動態的環境，雖然可能在幾年內成為董事，但建立成功企業所需的技能需要花費更多的時間和毅力來磨練。

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是首字母縮寫詞，代表表現最佳的五家技術公司：Facebook，亞馬遜，蘋果，Netflix和Google。 這些技術巨頭在四個主要方面與初創公司不同： 效率，流程，責任和職業軌跡 。

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：在本節中，我們僅指FAANG公司的數據科學家，但是所描述的角色也代表了在其他擁有大量員工的大型高科技公司中的數據科學職位。

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.

全球技術超級大國擁有成千上萬的員工，他們全部執行自己獨特的任務。 精確測量工作輸出，并將團隊成員置于層次結構中。 從這個意義上講，工作生活充滿了訂單，任務被明確定義，員工向一位老板匯報工作，衡量員工的成功。 與起初職位的流動性相比，此角色更易于管理和理解。

處理 (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.

在一家經驗豐富且管理完善的公司中，從學術界或以前的工作到此職位的過渡將是無縫的。 訓練營是一種通用資源，可以使未來的員工具備跨部門的必要技能。

職責范圍 (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.

平均工作經驗將圍繞分析和創建儀表板。 無論是分析具有凝聚力的公司績效還是某個功能是否成功，數據分析工作都將非常簡單。

職業軌跡 (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，通常很難在FAANG公司攀登職業階梯。 但是，作為獨立承包商(IC)賺錢可能更容易； 該角色通常需要深入研究優化和生產產品 。 職業階梯與剛起步的職業階梯截然不同，晉升為董事職位可能需要數十年的努力 。

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，在亞馬遜，典型的職業階梯可能是從業務分析師到商業智能工程師再到數據科學家再到研究科學家，而每個后續職位的薪水都更高。 每個角色也有四個“階段”：I，II，III(高級)，IV(負責人)。 從這些公司的層次結構中可以看出，晉升有一條明確的途徑，但與初創企業進行類似的晉升相比，還有更多的“完成”階段。

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.

盡管確切定義在行業和國家/地區之間有所不同，但根據經濟合作與發展組織(OECD)的數據，中型企業通常擁有50至250名員工 。 這類公司可以看作是初創公司與FAANG公司之間的中間地帶。

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.

隨著初創公司的快速成長階段趨于平穩，公司開始感受到市場和競爭對手的壓力，中型公司將經歷被恰當地描述為“成長中的痛苦” 。 在員工方面，一顆平常心是初創企業的自由和舫的結構之間實現。 從這個意義上講，盡管數據科學家的角色旨在適應不同的需求，但同時要履行一系列明確的職責。

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，雖然負面因素在一個平衡的基礎上得到平衡，但收益也各不相同。 中型公司的數據科學家的平均薪水將高于初創公司，但低于FAANG的公司。 晉升機會也介于初創公司和FAANG之間。 雖然不能保證成為公司的主要貢獻者； 只要有耐心和毅力，就可以擴大團隊規模并為公司帶來巨大的價值。

摘要 (Summary)

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

您可能會問：“什么規模的公司最適合我？”

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?”

一個人理想的公司規模在很大程度上取決于該人的個人目標和優先事項-是付款，晉升還是多樣化的經歷？ 還是所有這些的混合體？ 盡管如此，鑒于全球數據科學革命正在不斷發展，仍然存在一個問題：“我如何找到數據科學工作？”

The answer is: Check out Interview Query!

答案是： 簽出面試查詢！

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

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