介紹 (Introduction)

Pinterest, Inc. is a social media web and mobile application company founded in 2009, headquartered in San Francisco, California. The company develops and operates software applications and systems, designed to enable the discovery and saving of information online using images, GIFs, and videos (known as Pins). It offers free registration, after which users are allowed to upload, save, sort, and manage images and other content, eg videos (pins), through a gallery of images known as pinboards.

Pinterest，Inc.是一家社交媒體網(wǎng)絡(luò)和移動(dòng)應(yīng)用程序公司，成立于2009年，總部位于加利福尼亞州舊金山。 該公司開(kāi)發(fā)和運(yùn)營(yíng)軟件應(yīng)用程序和系統(tǒng)，旨在使用圖像，GIF和視頻(稱為Pins)在線發(fā)現(xiàn)和保存信息。 它提供免費(fèi)注冊(cè)，之后允許用戶通過(guò)稱為插腳板的圖像庫(kù)上載，保存，分類和管理圖像及其他內(nèi)容，例如視頻(圖釘)。

Its average user-ship has grown steadily since its inception, as audiences frequently turn to the platform for “planning social activities, shopping, learning things through how-to posts, and planning out life’s moments with boards for visual inspiration”.

自成立以來(lái)，它的平均用戶數(shù)量一直穩(wěn)定增長(zhǎng)，因?yàn)橛^眾經(jīng)常轉(zhuǎn)向該平臺(tái)，以“規(guī)劃社交活動(dòng)，購(gòu)物，通過(guò)how-to帖子學(xué)習(xí)事物，并通過(guò)視覺(jué)規(guī)劃板塊的生活時(shí)刻”。

As of the 4th quarter of 2019, Pinterest’s active average monthly users crossed 335 million worldwide, with over 175 billion items pinned on over 3 billion virtual pinboards. With this information, it is not far-fetched to imagine the massive amount of data generated daily. Data Science is at the core of Pinterest products and services, and data scientists at Pinterest leverage the most advanced analytics tools and machine learning models to make sense of this data for guiding business decisions.

截至2019年第4季度，Pinterest活躍平均月度用戶在全球范圍內(nèi)已超過(guò)3.35億，其中超過(guò)1,300億個(gè)項(xiàng)目固定在超過(guò)30億個(gè)虛擬固定板上。 有了這些信息，可以想象每天產(chǎn)生的大量數(shù)據(jù)并非難事。 數(shù)據(jù)科學(xué)是Pinterest產(chǎn)品和服務(wù)的核心，Pinterest數(shù)據(jù)科學(xué)家利用最先進(jìn)的分析工具和機(jī)器學(xué)習(xí)模型來(lái)利用這些數(shù)據(jù)來(lái)指導(dǎo)業(yè)務(wù)決策。

Pinterest數(shù)據(jù)科學(xué)角色 (The Data Science Role at Pinterest)

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Even now, Pinterest is still a growing company with many teams and departments working on key features, products, and services for improving customer experiences.

即使到現(xiàn)在，Pinterest仍是一家成長(zhǎng)中的公司，擁有許多團(tuán)隊(duì)和部門致力于關(guān)鍵功能，產(chǎn)品和服務(wù)，以改善客戶體驗(yàn)。

The data science team at Pinterest occasionally collaborates with other teams to design experiments around almost every user-facing feature to help make sense of the huge customer data generated daily, driving decision making and providing business-impact insights. As a result of this, data scientist roles at Pinterest are hugely determined by the assigned team. However, general data scientist roles at Pinterest span across experimentation and statistical modelling, basic business analytics and data visualization, machine learning and deep learning theories.

Pinterest數(shù)據(jù)科學(xué)團(tuán)隊(duì)有時(shí)會(huì)與其他團(tuán)隊(duì)合作，圍繞幾乎所有面向用戶的功能設(shè)計(jì)實(shí)驗(yàn) ，以幫助理解每天生成的龐大客戶數(shù)據(jù)，從而推動(dòng)決策制定并提供對(duì)業(yè)務(wù)有影響的見(jiàn)解 。 因此，Pinterest數(shù)據(jù)科學(xué)家角色很大程度上由指派的團(tuán)隊(duì)決定。 但是，Pinterest一般數(shù)據(jù)科學(xué)家角色橫跨實(shí)驗(yàn)和統(tǒng)計(jì)建模，基本業(yè)務(wù)分析和數(shù)據(jù)可視化，機(jī)器學(xué)習(xí)和深度學(xué)習(xí)理論 。

Interested in data science at another company with huge amounts of user data? Check out “The Deloitte Data Scientist Interview” article!

對(duì)另一家擁有大量用戶數(shù)據(jù)的公司的數(shù)據(jù)科學(xué)感興趣？ 查看“德勤數(shù)據(jù)科學(xué)家訪談”文章！

必備技能 (Required Skills)

Pinterest hires only qualified Data Scientists with at least 3 years (6+ years for a lead role) of industry experience in relevant data science projects. Requirements for hire are very specific depending on the job role for the team and as such, it helps to have specific industry experience that aligns with the role on the team.

Pinterest僅聘請(qǐng)?jiān)谙嚓P(guān)數(shù)據(jù)科學(xué)項(xiàng)目中具有至少3年行業(yè)經(jīng)驗(yàn)(領(lǐng)導(dǎo)角色至少6年)的合格數(shù)據(jù)科學(xué)家。 聘用要求非常具體，具體取決于團(tuán)隊(duì)的工作角色，因此，這有助于獲得與團(tuán)隊(duì)中的角色保持一致的特定行業(yè)經(jīng)驗(yàn)。

Other relevant qualifications include:

其他相關(guān)資格包括：

• Advanced Degree (MS or PhD) in a quantitative field or related fields.
  定量領(lǐng)域或相關(guān)領(lǐng)域的高級(jí)學(xué)位(MS或PhD)。
• 3+ years experience (6+ years for a senior role) of industry experience and a proven track record of applying statistical methods to solve real-world problems using big data.
  3年以上行業(yè)經(jīng)驗(yàn)(高級(jí)職位6年以上)，并具有使用統(tǒng)計(jì)方法解決大數(shù)據(jù)實(shí)際問(wèn)題的可靠記錄。
• Industry experience in both online and offline experimentation.
  在線和離線實(shí)驗(yàn)的行業(yè)經(jīng)驗(yàn)。
• Experience managing and analyzing structured and unstructured data with SQL, R or Python, and using software packages like SPSS, STATA, etc.
  具有使用SQL，R或Python以及使用SPSS，STATA等軟件包管理和分析結(jié)構(gòu)化和非結(jié)構(gòu)化數(shù)據(jù)的經(jīng)驗(yàn)。
• Extensive experience with applying deep learning methods in settings like recommender systems, time-series, user modelling, image recognition, graph representation learning, and natural language processing.
  在推薦系統(tǒng)，時(shí)間序列，用戶建模，圖像識(shí)別，圖形表示學(xué)習(xí)和自然語(yǔ)言處理等設(shè)置中應(yīng)用深度學(xué)習(xí)方法的豐富經(jīng)驗(yàn)。
• Experience with learning from ranking labels (i.e. triplet learning, metric learning, etc.) and deploying ranking models (i.e. learning-to-rank).
  具有從排名標(biāo)簽中學(xué)習(xí)的經(jīng)驗(yàn)(即三元組學(xué)習(xí)，度量學(xué)習(xí)等)以及部署排名模型(即按等級(jí)學(xué)習(xí))的經(jīng)驗(yàn)。
• Ability to lead initiatives across multiple product areas and communicate findings with leadership and product teams.
  能夠領(lǐng)導(dǎo)多個(gè)產(chǎn)品領(lǐng)域的計(jì)劃，并與領(lǐng)導(dǎo)和產(chǎn)品團(tuán)隊(duì)交流發(fā)現(xiàn)結(jié)果。

Pinterest數(shù)據(jù)科學(xué)團(tuán)隊(duì)是什么？ (What are the data science teams at Pinterest?)

Data scientist roles and functions at Pinterest run across a wide range of teams and fields related to data science. The title “data scientist” at Pinterest encompasses multiple roles and functions ranging from product focused-analytics to more technical machine learning and deep learning functions.

Pinterest數(shù)據(jù)科學(xué)家角色和職能遍布與數(shù)據(jù)科學(xué)相關(guān)的眾多團(tuán)隊(duì)和領(lǐng)域。 Pinterest標(biāo)題為“數(shù)據(jù)科學(xué)家”，涵蓋多個(gè)角色和功能，范圍從以產(chǎn)品為重點(diǎn)的分析到更加技術(shù)性的機(jī)器學(xué)習(xí)和深度學(xué)習(xí)功能 。

Based on the assigned team, the function of a data Scientist at Pinterest may include:

根據(jù)指定的團(tuán)隊(duì)，Pinterest數(shù)據(jù)科學(xué)家的職能可能包括：

• Engineering (Offline Experimentation): Leveraging advanced data analytic concepts to solve key measurement challenges involving the offline evaluation of data, from fine-tuning measurement techniques to defining approaches for creating meaningful measurements of value for new and existing new products.

  工程(離線實(shí)驗(yàn)) ：利用高級(jí)數(shù)據(jù)分析概念來(lái)解決涉及離線評(píng)估數(shù)據(jù)的關(guān)鍵測(cè)量挑戰(zhàn)，從微調(diào)測(cè)量技術(shù)到定義為新產(chǎn)品和現(xiàn)有新產(chǎn)品創(chuàng)建有意義的價(jià)值測(cè)量方法。

• Engineering (Ads Experimentation): Designing and building models, mechanisms, and metrics to make sound product decisions through experimentation with the end goal of surfacing high-quality ads for every Pinner.

  工程(廣告實(shí)驗(yàn)) ：設(shè)計(jì)和構(gòu)建模型，機(jī)制和指標(biāo)，以通過(guò)實(shí)驗(yàn)做出合理的產(chǎn)品決策，最終目標(biāo)是為每個(gè)Pinner展示高質(zhì)量的廣告。

• Business Operation and Strategy: Leveraging business analytics to drive critical business insights for a better understanding of Pinners, Partners, and products.

  業(yè)務(wù)運(yùn)營(yíng)和策略 ：利用業(yè)務(wù)分析來(lái)推動(dòng)關(guān)鍵業(yè)務(wù)見(jiàn)解，以更好地了解Pinners，合作伙伴和產(chǎn)品。

• Ads Quality Ranking team: Applying experimentation, quantitative analysis, data mining and data visualization techniques to improve the quality and relevance of ads on Pinterest.

  廣告質(zhì)量排名小組 ：應(yīng)用實(shí)驗(yàn)，定量分析，數(shù)據(jù)挖掘和數(shù)據(jù)可視化技術(shù)來(lái)提高Pinterest上廣告的質(zhì)量和相關(guān)性。

• Ads Intelligence: Developing machine learning models, systems, and features that help advertisers maximize the return on investment of ad campaigns on Pinterest through recommendations, tools, and insights.

  廣告智能 ：開(kāi)發(fā)機(jī)器學(xué)習(xí)模型，系統(tǒng)和功能，以幫助廣告客戶通過(guò)推薦，工具和見(jiàn)解最大化廣告活動(dòng)在Pinterest上的投資回報(bào)。

面試過(guò)程 (The Interview Process)

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The interview process starts with an initial phone screen with a recruiter or a hiring manager, and if all goes well, a technical screen with a data scientist or a data engineer will be scheduled. After passing the technical screen, you then proceed to the onsite interview, which comprises five back to back interview rounds with a lunch break in between.

面試過(guò)程從招募人員或招聘經(jīng)理的初始電話屏幕開(kāi)始，如果一切順利，將安排與數(shù)據(jù)科學(xué)家或數(shù)據(jù)工程師的技術(shù)屏幕。 通過(guò)技術(shù)屏幕后，您可以繼續(xù)進(jìn)行現(xiàn)場(chǎng)采訪，其中包括五次背對(duì)背的采訪回合，中間有午餐休息時(shí)間。

初始畫面 (Initial Screen)

This is a 30 minute initial phone conversation with a recruiter, detailing your technical background, your past relevant projects, and a quick assessment of your skill sets based on your resume. Within this interview, the interviewer will also discuss with you the roles on the team and Pinterest culture.

這是與招聘人員進(jìn)行的30分鐘的初始電話交談，詳細(xì)介紹了您的技術(shù)背景，您過(guò)去的相關(guān)項(xiàng)目以及根據(jù)履歷快速評(píng)估您的技能。 在這次面試中，面試官還將與您討論團(tuán)隊(duì)中的角色和Pinterest文化。

Sample Questions:

樣題：

• Tell me about yourself.
  說(shuō)說(shuō)你自己。
• Talk about one of your past work experiences.
  談?wù)撃^(guò)去的工作經(jīng)驗(yàn)之一。

技術(shù)畫面 (Technical Screen)

The technical screen is an hour-long interview with a data scientist, with discussion revolving around a past project, the approaches you used, and how you solved certain challenges.

技術(shù)屏幕是對(duì)數(shù)據(jù)科學(xué)家進(jìn)行的一個(gè)小時(shí)的采訪，討論圍繞過(guò)去的項(xiàng)目，您使用的方法以及如何解決某些挑戰(zhàn)進(jìn)行。

There will also be some light SQL coding in this interview. Pinterest uses “Karat” for almost all their technical interviews and the Data Scientist technical screening is also done using the shared screen Karat platform.

在這次采訪中還將有一些簡(jiǎn)單SQL編碼。 Pinterest在幾乎所有的技術(shù)采訪中都使用“ Karat” ，并且還使用共享屏幕Karat平臺(tái)來(lái)進(jìn)行Data Scientist技術(shù)篩選。

At a minimum we recommend reviewing this article about “Three SQL Concepts you Must Know to Pass the Data Science Interview” on Interview Query to prepare for your interview.

我們至少建議您 閱讀有關(guān)“ 采訪查詢 ”中 有關(guān) “ 通過(guò)數(shù)據(jù)科學(xué)采訪必須知道的三個(gè)SQL概念 ”的文章 ，以為您的采訪做準(zhǔn)備。

現(xiàn)場(chǎng)采訪 (Onsite Interview)

The onsite interview is the last interview stage for the Pinterest Data Scientist interview. It consists of five back-to-back interview rounds, split between a SQL interview, a statistics and probability interview, one coding interview, and a behavioral interview. All interview rounds in the onsite stage last approximately 45 minutes, with a lunch break in between.

現(xiàn)場(chǎng)采訪是Pinterest數(shù)據(jù)科學(xué)家采訪的最后一個(gè)采訪階段。 它由五次背對(duì)背訪談構(gòu)成，分為SQL訪談， 統(tǒng)計(jì) 和概率訪談，一個(gè)編碼訪談和行為訪談。 現(xiàn)場(chǎng)階段的所有采訪都持續(xù)約45分鐘，中間有午餐時(shí)間。

注意事項(xiàng) (Notes and Tips)

Pinterest Data Scientist interviews aim to assess candidates’ ability to design experiments for assessing product performance, build models at scale, and apply data science concepts to drive growth and provide business-impacts insights. Therefore, interview questions are standardized and cover a wide range of data science concepts. Brush up on your knowledge of statistics and probability, hypothesis testing, time series modelling, A/B testing, experimental designs, SQL, and predictive modelling concepts.

Pinterest數(shù)據(jù)科學(xué)家面試旨在評(píng)估候選人設(shè)計(jì)實(shí)驗(yàn)的能力， 以評(píng)估產(chǎn)品性能，大規(guī)模建立模型，以及應(yīng)用數(shù)據(jù)科學(xué)概念來(lái)推動(dòng)增長(zhǎng)并提供對(duì)業(yè)務(wù)影響的見(jiàn)解 。 因此，面試問(wèn)題是標(biāo)準(zhǔn)化的，涵蓋了廣泛的數(shù)據(jù)科學(xué)概念。 掌握統(tǒng)計(jì)和概率，假設(shè)檢驗(yàn)，時(shí)間序列建模，A / B檢驗(yàn)，實(shí)驗(yàn)設(shè)計(jì)，SQL和預(yù)測(cè)建模概念的知識(shí)。

Practicing interview questions from Interview Query can better prepare you for the technical aspect.

通過(guò)“ 面試查詢”練習(xí)面試問(wèn)題可以更好地為您做好技術(shù)方面的準(zhǔn)備。

Pinterest has an employee-focused ecosystem, which provides a friendly work environment for all. In a 2019 article, Pinterest was quoted as “ the nicest company in Silicon Valley … The culture stands out from other high-growth tech companies where confrontation and debate are actively encouraged”. Culture-wise, Pinterest offers a really progressive work environment where employees (technical or not) can grow and thrive.

Pinterest擁有以員工為中心的生態(tài)系統(tǒng)，為所有人提供了友好的工作環(huán)境。 在2019年的一篇文章中，Pinterest被評(píng)為“ 硅谷最好的公司 ……這種文化與其他那些積極鼓勵(lì)對(duì)抗和辯論的高科技公司脫穎而出”。 從文化角度講，Pinterest提供了一個(gè)真正進(jìn)步的工作環(huán)境，員工(無(wú)論技術(shù)與否)都可以成長(zhǎng)并蓬勃發(fā)展。

Another company with great work culture is LinkedIn. Check out this guide about “LinkedIn Data Science Interview Questions”.

擁有良好工作文化的另一家公司是LinkedIn。 查閱有關(guān)“ LinkedIn數(shù)據(jù)科學(xué)面試問(wèn)題 ”的指南。

Pinterest數(shù)據(jù)科學(xué)面試問(wèn)題： (Pinterest Data Science Interview Questions:)

• Give an array of unsorted random numbers (decimals), find the interquartile distance.
  給出一個(gè)未排序的隨機(jī)數(shù)(十進(jìn)制)數(shù)組，找到四分位數(shù)距離。
• Write a SQL query to count the number of unique users per day who logged in from both iPhone and web, where iPhone logs and web logs are in distinct relations.
  編寫一個(gè)SQL查詢來(lái)計(jì)算每天從iPhone和Web登錄的唯一身份用戶數(shù)，其中iPhone日志和Web日志之間存在明顯的關(guān)系。
• Your product manager noticed a dip in a specific metric. How do you go about investigating what may have caused the dip?
  您的產(chǎn)品經(jīng)理注意到特定指標(biāo)有所下降。 您如何調(diào)查可能導(dǎo)致下降的原因？

Originally published at https://www.interviewquery.com on August 4, 2020.

最初于 2020年8月4日 發(fā)布在 https://www.interviewquery.com 。

翻譯自: https://towardsdatascience.com/the-pinterest-data-scientist-interview-b5cdf12e870f

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