通才與專家

Throughout my 10-year career, I have seen people often spend their time and energy in passionate debates about what data science can deliver, and what data scientists do or do not do. I submit that these are the wrong questions to focus on when you are looking to hire for your data department. In actuality, your current value proposition determines what data science means for your company, and hence the role and responsibilities of a data scientist in your ecosystem.

在我的10年職業生涯中，我看到人們經常花費時間和精力進行激烈的辯論，討論數據科學可以提供什么以及數據科學家可以做什么或不可以做什么。 我認為，這是您要為數據部門招聘時要重點關注的錯誤問題。 實際上，您當前的價值主張決定了數據科學對您的公司意味著什么，從而決定了數據科學家在您的生態系統中的角色和職責。

Instead of embarking on an impossible task to define data scientists in absolute terms, and hoping for an industry-wide consensus on it, think about the role in an alternative way. Define your company’s data needs in terms of data generalists and data specialists.

不要以絕對的術語來完成定義數據科學家的不可能的任務，而是希望在整個行業達成共識，而是以另一種方式考慮角色。 根據數據專家和數據專家定義公司的數據需求。

Some entities (be it people or companies, etc.) consider data scientists strictly as data generalists, and others as data specialists.But a data scientist can be either. Data science is about using data to provide value (such as money, growth, reputation, etc.) to an organization, and to provide value, sometimes you need a data generalist, and sometimes a data specialist.

一些實體(無論是個人還是公司等)都將數據科學家嚴格地視為數據通才，而另一些實體則將其視為數據專家。 數據科學是關于使用數據為組織提供價值(例如金錢，增長，聲譽等)，并提供價值，有時您需要數據通才，有時需要數據專家。

Data generalists are breadth focused and are highly capable in conducting ad hoc analyses, extracting insights from data, and helping direct business questions. They can function reactively, like looking back at the data and reporting trends, and can also operate proactively, by exploring more open-ended questions, and looking into the future. Their skill set spans exploratory data analysis techniques, scripting and modeling, visualization and reporting.

數據通才專注于廣度，并且具有進行臨時分析，從數據中提取見解以及幫助解決業務問題的能力。 他們可以做出React，就像回顧數據和報告趨勢一樣，也可以通過探索更多開放性問題并展望未來來主動行動。 他們的技能涵蓋了探索性數據分析技術，腳本和建模，可視化和報告。

Data specialists are depth focused and have expertise in automation, optimization, machine learning, and performance tuning. They come in when a problem is well scoped, and a process well understood, and take it to the next level of optimization, enabling operation that requires minimal human interaction.

數據專家專注于深度，并且在自動化，優化，機器學習和性能調整方面具有專業知識。 當問題的范圍很廣，流程得到了很好的理解時，它們就會出現，并將其帶入下一個優化級別，從而使操作所需的人力最少。

It is important to recognize that there is no implicit hierarchy between data generalists and specialists. They each focus on a different set of problems, and therefore provide a different set of solutions, while being equally valuable to a company.

重要的是要認識到數據通才和專家之間沒有隱含的層次結構。 他們每個人都專注于一組不同的問題，因此提供了一組不同的解決方案，同時對一家公司同樣有價值。

Every company needs to determine the appropriate mix of data specialists and data generalists for their goals.

每個公司都需要確定適合其目標的數據專家和數據專家的組合。

Start with a simple question: Based on your current needs, do you need a data generalist or a data specialist? And then make that expectation known — starting with the job posting.

從一個簡單的問題開始：根據您當前的需求，您需要數據通才還是數據專家？ 然后，從職位發布開始，使這一期望成為現實。

Instead of copy-pasting requirements from another data scientist job advertisement, or creating one with a superset of requirements from multiple similar postings, it is paramount that the company intentionally defines its requirements. This is the single most important step that hiring companies can do to enable fulfilling careers and enhanced productivity.

公司必須有意識地定義其要求，而不是從另一個數據科學家的招聘廣告中粘貼要求，或者從多個相似的帖子中創建帶有要求的超集的公司。 這是招聘公司可以采取的最重要的單個步驟，以實現充實的職業并提高生產力。

For example, if you are focused on providing a single well-defined service, you may benefit from having a data specialist joining your ranks. They will help optimize and automate the task. On the other hand, if your product offering spans multiple domains, having data generalists may be more beneficial. They are better equipped to provide overarching product analyses, monitoring, and making growth recommendations to the business. Yearly targets, quarterly goals, and 3–6–9 planning meetings can help you track of such needs, and adjust accordingly.

例如，如果您專注于提供單一的定義明確的服務，則可以從數據專家的行列中受益。 他們將幫助優化和自動化任務。 另一方面，如果您提供的產品跨越多個領域，那么讓數據通才更為有益。 他們具備更好的能力來提供總體產品分析，監視并為業務提出增長建議。 年度目標，季度目標和3–6–9計劃會議可以幫助您跟蹤此類需求并進行相應調整。

So, do you need to hire a data scientist? Before you do, determine which will provide the most value to your company at the moment: a data generalist or a specialist. No matter what you choose to call the role, spend some time defining the breadth or depth of the expectations clearly. It will empower you to make the right hire, and also enable the potential employee to make informed decisions in line with their own goals.

那么，您需要聘請數據科學家嗎？ 在執行此操作之前，請確定哪個將為您的公司目前提供最大的價值：數據通才或專家。 無論您選擇用什么角色，都要花一些時間明確定義期望的廣度或深度。 它將使您能夠做出正確的聘用，并使潛在的員工能夠根據自己的目標做出明智的決定。

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A version of this article first appeared in BuiltIn, and has been republished with the author’s permission.

本文的一個版本首次出現在BuiltIn中，并且在作者許可下已重新發布。

翻譯自: https://towardsdatascience.com/so-you-are-ready-to-hire-a-data-scientist-9775153c44b5

通才與專家

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