初創(chuàng)公司怎么做銷售數(shù)據(jù)分析

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(gè)技術(shù)日新月異的世界中，數(shù)據(jù)科學(xué)家和分析人員的角色已經(jīng)出現(xiàn)，職責(zé)范圍從優(yōu)化Yelp等級(jí)到過(guò)濾Amazon建議和設(shè)計(jì)Facebook功能。 但是數(shù)據(jù)科學(xué)家到底在做什么？ 很少嚴(yán)格定義此角色的參數(shù)，但是面向數(shù)據(jù)的工作已成為所有技術(shù)公司成功的當(dāng)務(wù)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(chuàng)公司轉(zhuǎn)到中型公司或FAANG(Facebook，Amazon，Apple，Netflix，Google)時(shí)，您可能會(huì)遇到一系列陌生的新任務(wù)。

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(guān)于FAANG公司的信息嗎？ 閱讀有關(guān)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(shí)現(xiàn)這些公司類型之間的主要差異(工作類型，預(yù)期經(jīng)驗(yàn)，優(yōu)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(chuàng)業(yè)公司 (Startup Companies)

UnsplashUnsplash的啟動(dòng)公司辦公室空間

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(chuàng)公司描述了那些在快速發(fā)展的商業(yè)世界中新興，Swift開(kāi)發(fā)創(chuàng)新產(chǎn)品或服務(wù)的公司。 美國(guó)小企業(yè)管理局正式將初創(chuàng)公司描述為“通常以技術(shù)為導(dǎo)向并具有高增長(zhǎng)潛力的企業(yè)” ； 涉及員工，收入或市場(chǎng)的高增長(zhǎng)潛力。 這類公司在兩個(gè)方面很獨(dú)特： 工作多樣化和人數(shù)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(chuàng)公司中，數(shù)據(jù)科學(xué)角色幾乎不涉及任何事情。 它需要具備所有知識(shí)，包括數(shù)據(jù)工程，機(jī)器學(xué)習(xí)，分析，數(shù)據(jù)可視化以及傳統(tǒng)上不能稱為“數(shù)據(jù)科學(xué)”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(yíng)銷會(huì)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(chuàng)公司工作的最大好處是獲得和發(fā)展了各種技能 ，這在大型公司中很少見(jiàn)。 作為初創(chuàng)公司的數(shù)據(jù)科學(xué)家，期望承擔(dān)一些必須“弄清楚”的問(wèn)題。 這導(dǎo)致了許多自學(xué)，自定進(jìn)度，所有權(quán)和獨(dú)立性。

低人數(shù) (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(chuàng)公司的員工人數(shù)較少，因此隨著公司的成長(zhǎng)， 獲得晉升會(huì)容易得多。 但是，人數(shù)少是一把雙刃劍。 人數(shù)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(jiàn)的職業(yè)道路是從一家較大的公司開(kāi)始，獲得經(jīng)驗(yàn)并獲得更高的薪水，然后過(guò)渡到一家初創(chuàng)公司，以獲得更多樣化的經(jīng)驗(yàn)和職業(yè)發(fā)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(chuàng)公司的職業(yè)階梯可能更容易攀登，但您將沒(méi)有太多的工作與生活平衡。 初創(chuàng)公司更快的步伐會(huì)導(dǎo)致不斷變化和動(dòng)態(tài)的環(huán)境，雖然可能在幾年內(nèi)成為董事，但建立成功企業(yè)所需的技能需要花費(fèi)更多的時(shí)間和毅力來(lái)磨練。

FAANG公司 (FAANG Companies)

EducativeEducative的 FAANG公司徽標(biāo)

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(xiàn)最佳的五家技術(shù)公司：Facebook，亞馬遜，蘋果，Netflix和Google。 這些技術(shù)巨頭在四個(gè)主要方面與初創(chuàng)公司不同： 效率，流程，責(zé)任和職業(yè)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(jié)中，我們僅指FAANG公司的數(shù)據(jù)科學(xué)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(shù)據(jù)科學(xué)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(shù)超級(jí)大國(guó)擁有成千上萬(wàn)的員工，他們?nèi)繄?zhí)行自己獨(dú)特的任務(wù)。 精確測(cè)量工作輸出，并將團(tuán)隊(duì)成員置于層次結(jié)構(gòu)中。 從這個(gè)意義上講，工作生活充滿了訂單，任務(wù)被明確定義，員工向一位老板匯報(bào)工作，衡量員工的成功。 與起初職位的流動(dòng)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(jīng)驗(yàn)豐富且管理完善的公司中，從學(xué)術(shù)界或以前的工作到此職位的過(guò)渡將是無(wú)縫的。 訓(xùn)練營(yíng)是一種通用資源，可以使未來(lái)的員工具備跨部門的必要技能。

職責(zé)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(jīng)驗(yàn)將圍繞分析和創(chuàng)建儀表板。 無(wú)論是分析具有凝聚力的公司績(jī)效還是某個(gè)功能是否成功，數(shù)據(jù)分析工作都將非常簡(jiǎn)單。

職業(yè)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(yè)階梯。 但是，作為獨(dú)立承包商(IC)賺錢可能更容易； 該角色通常需要深入研究優(yōu)化和生產(chǎn)產(chǎn)品 。 職業(yè)階梯與剛起步的職業(yè)階梯截然不同，晉升為董事職位可能需要數(shù)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(yè)階梯可能是從業(yè)務(wù)分析師到商業(yè)智能工程師再到數(shù)據(jù)科學(xué)家再到研究科學(xué)家，而每個(gè)后續(xù)職位的薪水都更高。 每個(gè)角色也有四個(gè)“階段”：I，II，III(高級(jí))，IV(負(fù)責(zé)人)。 從這些公司的層次結(jié)構(gòu)中可以看出，晉升有一條明確的途徑，但與初創(chuàng)企業(yè)進(jìn)行類似的晉升相比，還有更多的“完成”階段。

中型公司 (Mid-size Companies)

UnsplashUnsplash的辦公室

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(yè)和國(guó)家/地區(qū)之間有所不同，但根據(jù)經(jīng)濟(jì)合作與發(fā)展組織(OECD)的數(shù)據(jù)，中型企業(yè)通常擁有50至250名員工 。 這類公司可以看作是初創(chuàng)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(chuàng)公司的快速成長(zhǎng)階段趨于平穩(wěn)，公司開(kāi)始感受到市場(chǎng)和競(jìng)爭(zhēng)對(duì)手的壓力，中型公司將經(jīng)歷被恰當(dāng)?shù)孛枋鰹?strong>“成長(zhǎng)中的痛苦” 。 在員工方面，一顆平常心是初創(chuàng)企業(yè)的自由和舫的結(jié)構(gòu)之間實(shí)現(xiàn)。 從這個(gè)意義上講，盡管數(shù)據(jù)科學(xué)家的角色旨在適應(yīng)不同的需求，但同時(shí)要履行一系列明確的職責(zé)。

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(fù)面因素在一個(gè)平衡的基礎(chǔ)上得到平衡，但收益也各不相同。 中型公司的數(shù)據(jù)科學(xué)家的平均薪水將高于初創(chuàng)公司，但低于FAANG的公司。 晉升機(jī)會(huì)也介于初創(chuàng)公司和FAANG之間。 雖然不能保證成為公司的主要貢獻(xiàn)者； 只要有耐心和毅力，就可以擴(kuò)大團(tuán)隊(duì)規(guī)模并為公司帶來(lái)巨大的價(jià)值。

摘要 (Summary)

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

您可能會(huì)問(wèn)：“什么規(guī)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(gè)人理想的公司規(guī)模在很大程度上取決于該人的個(gè)人目標(biāo)和優(yōu)先事項(xiàng)-是付款，晉升還是多樣化的經(jīng)歷？ 還是所有這些的混合體？ 盡管如此，鑒于全球數(shù)據(jù)科學(xué)革命正在不斷發(fā)展，仍然存在一個(gè)問(wèn)題：“我如何找到數(shù)據(jù)科學(xué)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(fā)布在 https://www.interviewquery.com 。

翻譯自: https://towardsdatascience.com/data-science-at-a-startup-vs-faang-company-19af9e1d6757

初創(chuàng)公司怎么做銷售數(shù)據(jù)分析

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