corba的興衰

意見 (Opinion)

目錄 (Table of Contents)

Introduction

介紹

Salary and Growth

薪資與增長

Summary

摘要

介紹 (Introduction)

In the past five years, data science salary cumulative growth has varied between 12% in the United States, according to Glassdoor’s job market report [2]. Between those five years, there have been certain months of significant increase and decrease. In this article, I will outline the key metrics that occur at certain months and years of these five years, including median base pay, cumulative growth, and year-over-year growth. Somewhat similar careers, like software engineering, have seen steady inclines over the same period, so it is important to note the volatility of data science salaries.

根據Glassdoor的就業市場報告[2]，在過去的五年中，美國數據科學專業的薪資累積增長在12％之間變化。 在這五年之間，有幾個月出現了明顯的增加和減少。 在本文中，我將概述這五年中某些月份和年份發生的關鍵指標，包括基本工資中位數，累計增長和逐年增長。 某些類似的職業，例如軟件工程，在同一時期出現了穩定的增長趨勢，因此，重要的是要注意數據科學人員薪資的波動性。

I will speculate as to why there is either an increase or decrease in growth along with its respective median base salary for each significant period of time. There are many reasons as to why these changes have occurred, and it is interesting to look at the differences between months and years for data science salary, spanning over $10,000 total. If you would like to know more salary information on similar positions like data analyst and software engineer, you can refer to the same Glassdoor job market report by following the link at the end of this article.

我將推測為什么在每個重要的時期內，增長率及其相應的基本工資中位數都會增加或減少。 發生這些變化的原因有很多，有趣的是看看數據科學月薪和年薪之間的差異，總額超過10,000美元。 如果您想了解有關數據分析師和軟件工程師等類似職位的更多薪資信息，可以通過以下文章末尾的鏈接引用同一份Glassdoor工作市場報告。

薪資與增長 (Salary and Growth)

I will be discussing the key metrics over the rise and fall and rise again of data science salaries below.

我將在下面討論數據科學薪資的起伏和上升趨勢的關鍵指標。

上升 (Rise)

June 2015

2015年6月

The initial rise of the five year period is June of 2015. This month recorded a median base salary of $95,798, with a cumulative growth of 6.2%. There could be several reasons to explain this growth. While it is not recorded, I can speculate on what has caused this change in cumulative pay growth.

五年期間的最初增長是2015年6月。本月基本工資中位數為$ 95,798 ，累計增長6.2％ 。 可能有幾個原因可以解釋這種增長。 雖然沒有記錄，但我可以推測是什么原因導致了累積工資增長的這種變化。

I would suggest that the spike in growth is from companies realizing how powerful and popular this career in data science is. Once companies hire more and more data scientists, it becomes more competitive not only for the applicant, but for the employer as well. I am considering that in 2015, in this case, data science is well-established, and applicants feel more confident in demanding a higher salary, as well as companies allocating more of their budget to data science careers after seeing or hearing about wide success with this position on businesses.

我建議增長的高峰來自公司，他們意識到數據科學事業的強大和流行。 一旦公司聘請了越來越多的數據科學家，它不僅對申請者而且對雇主都更具競爭力。 我考慮的是，在這種情況下，2015年的數據科學已經建立了良好的基礎，申請人對要求更高的薪水更有信心，而且在看到或聽到了廣泛的成功經驗之后，公司也將更多預算分配給數據科學職業這個職位對企業。

In some cases, you could argue that one data scientist could perform the function of two analysts from the automation of common processes with programming language like Python, so why not pay one person a little more instead of having two people cost your business even more in the long run? Of course, all companies are different in some ways, along with their respective roles, so this could be beneficial or detrimental. Additionally, data analysts can sometimes have considerably different tasks, processes, and impacts.

在某些情況下，您可能會爭辯說，一位數據科學家可以使用像Python這樣的編程語言來實現通用流程的自動化，從而執行兩位分析師的職能，因此，為什么不花一個人多付錢，而不是讓兩個人花更多的錢在您的企業中從長遠來看？ 當然，所有公司在某些方面以及他們各自的角色上都是不同的，因此這可能是有益的或有害的。 此外，數據分析師有時可能會有截然不同的任務，流程和影響。

Once again, here are the metrics for the first rise:

再一次，這是第一次上升的指標：

Rise of June 2015median base salary: $95,798cumulative growth: 6.2%

秋季 (Fall)

June 2016

2016年六月

Once companies hired their first data scientist, they sought out to hire more.

一旦公司聘請了第一位數據科學家，他們便尋求聘用更多的人。

Perhaps this trend meant that they already had a senior data scientist and the next logical choice was to look for a junior data scientist that could be acquired for a smaller salary.

也許這種趨勢意味著他們已經有一位高級數據科學家，下一個合乎邏輯的選擇是尋找一個初級的數據科學家，可以以較低的薪水獲得它。

This fall in June of 2016 was considerably substantial in that the median base salary dropped to $88,649 with a cumulative growth of -1.7% and a year-over-year growth of -7.5%. Yes, those last two statistics were negative. While I do not know for certain the cause of this significant drop in pay, I do know that there were going to be better months and years ahead. As a data scientist myself, I would create a dataset to isolate key, significant features like the following:

2016年6月的這個秋天相當可觀，基本工資中位數降至88,649美元 ，累計增長-1.7％ ，同比增長-7.5％ 。 是的，最后兩個統計數據均為負數。 雖然我不確定工資大幅下降的原因，但我確實知道未來幾個月和幾年會更好。 作為數據科學家本人，我將創建一個數據集以隔離關鍵的重要功能，例如：

• location
  位置
• demographic
  人口統計
• spread of junior and senior roles
  初級和高級職位的傳播
• split of data science and machine learning positions
  數據科學和機器學習職位的劃分
• range of salary expanding
  薪資范圍擴大
• negative press
  負面新聞
• employee reviews
  員工評價
• budget cuts
  削減預算
• budget allocations
  預算撥款
• errors in reporting pay
  工資報錯
• current events
  現在發生的事
• inflation
  通貨膨脹
• etc.
  等等

As you can see, there are several different ways of dissecting this decrease in pay. Luckily, the fall did not last long, and a huge rise would top that initial rise. Here are those metrics highlighted once more:

如您所見，有幾種不同的方法可以剖析這種薪資下降的情況。 幸運的是，這種下降并沒有持續很長時間，而且大幅上升將超過最初的上升。 這些指標再次突出顯示：

Fall of June 2016median base salary: $88,649cumulative growth: -1.7%year-over-year growth: -7.5%

再次上升 (Rise Again)

June 2020

2020年6月

It took a few years to see this rise again, which occurred recently in June 2020. Perhaps with COVID-19, tech roles became of more focus as employees demanded to work from home, or were required to. Customer-facing roles perhaps declined, as many new positions would need to be performed individually at home, over a video conferencing platform. Nearly $100,000, the median base pay for data scientists in this month was $99,674 with a cumulative growth of 10.5% and a year-over-year growth of 5.5%. This rise again is of course great news for data scientists. If you have not noticed yet, it is important to note that all of these key dates have been in the month of June of their respective years, perhaps it is just a coincidence, but it would be interesting to know why this trend and pattern occurred.

幾年后才再次出現這種情況，這種情況最近發生在2020年6月。也許是在COVID-19的情況下，隨著員工要求在家中工作或被要求在家工作，技術角色變得更加重要。 面向客戶的角色可能會下降，因為需要通過視頻會議平臺在家中單獨執行許多新職位。 近十萬美元，本月數據科學家的基本薪資中位數為$ 99,674 ，累計增長10.5％ ，同比增長5.5％ 。 對于數據科學家來說，再次上升當然是個好消息。 如果您尚未注意到，則需要注意的是，所有這些關鍵日期都是在各自年份的6月，也許這只是一個巧合，但是了解為什么會出現這種趨勢和模式會很有趣。 。

The summarized information is here for the rise again:

摘要信息再次出現在這里：

Rise Again of June 2020median base salary: $99,674cumulative growth: 10.5%year-over-year growth: 5.5%

摘要 (Summary)

Screenshot of Google Data Studio by Author [3].作者[3]的Google Data Studio屏幕截圖。

For easy viewing, the chart above summarizes the key dates along with their respective metrics of median base salary, cumulative growth, and year-over-year growth. This chart was made in Google Data Studio and covers the key points in time discussed in this article. If you would like to see a more detailed time series chart with more months and years including not only the data science statistics, but other technology position statistics, follow the link from Glassdoor in the references section below.

為了便于查看，上表總結了關鍵日期及其各自的基本工資中位數，累積增長和逐年增長指標。 該圖表是在Google Data Studio中制作的，涵蓋了本文中討論的關鍵時間點。 如果您想查看更詳細的時序圖，包括更多月和幾年的時間，不僅包括數據科學統計信息，還包括其他技術位置統計信息，請按照下面參考部分中的Glassdoor鏈接。

As for the future of data science salary, it could be tricky to predict with the pandemic occurring, and companies that were once steady, are now volatile in themselves. Perhaps, data science pay will rise with more tech companies performing better, or the salary will decline as more and more companies, in general, are declining. Additionally, data science positions could soon offer more or less pay based on their split in specific requirements for the position. For example, a data scientist role in 2025 could reduce to a median base salary of $95,000, while a machine learning engineer role could increase to $105,000. This change could result in response to companies needing to rely more on the deployment of models and less face-to-face interactions.

至于數據科學領域的薪資前景，要預測這種大流行的發生可能會很棘手，而且曾經很穩定的公司現在已經變得不穩定。 也許，隨著越來越多的科技公司表現更好，數據科學的薪資將會增加，或者隨著越來越多的公司總體而言薪水的下降，薪水將會下降。 此外，根據數據科學職位的具體要求，他們很快就會提供或多或少的報酬。 例如，到2025年，數據科學家的角色的基本年薪中位數可降低至95,000美元，而機器學習工程師的角色的中位數則可增至105,000美元。 這種變化可能會導致企業需要更多地依賴模型的部署和更少的面對面交互。

In this article, we discussed the rise, fall, and rise again of data science salaries. There are several reasons for these changes in pay growth over the past five years. Feel free to comment down below on why you think this median pay changes so frequently. Keep in mind, this highly changing trend was not the same for similar roles in technology like software engineering, systems engineering, and web developer.

在本文中，我們討論了數據科學人員薪水的上升，下降和再次上升。 在過去五年中，薪資增長發生這些變化的原因有很多。 請隨意在下方評論為什么您認為中位數工資變化如此頻繁。 請記住，對于像軟件工程，系統工程和Web開發人員這樣的技術中的類似角色，這種高度變化的趨勢并不相同。

I hope you found my article useful and interesting. Thank you for reading my article!

希望您發現我的文章有用且有趣。 感謝您閱讀我的文章！

翻譯自: https://towardsdatascience.com/the-rise-and-fall-and-rise-again-of-data-science-salaries-8350d872ba9d

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