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.是一家社交媒體網絡和移動應用程序公司，成立于2009年，總部位于加利福尼亞州舊金山。 該公司開發和運營軟件應用程序和系統，旨在使用圖像，GIF和視頻(稱為Pins)在線發現和保存信息。 它提供免費注冊，之后允許用戶通過稱為插腳板的圖像庫上載，保存，分類和管理圖像及其他內容，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，它的平均用戶數量一直穩定增長，因為觀眾經常轉向該平臺，以“規劃社交活動，購物，通過how-to帖子學習事物，并通過視覺規劃板塊的生活時刻”。

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已超過3.35億，其中超過1,300億個項目固定在超過30億個虛擬固定板上。 有了這些信息，可以想象每天產生的大量數據并非難事。 數據科學是Pinterest產品和服務的核心，Pinterest數據科學家利用最先進的分析工具和機器學習模型來利用這些數據來指導業務決策。

Pinterest數據科學角色 (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.

即使到現在，Pinterest仍是一家成長中的公司，擁有許多團隊和部門致力于關鍵功能，產品和服務，以改善客戶體驗。

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數據科學團隊有時會與其他團隊合作，圍繞幾乎所有面向用戶的功能設計實驗 ，以幫助理解每天生成的龐大客戶數據，從而推動決策制定并提供對業務有影響的見解 。 因此，Pinterest數據科學家角色很大程度上由指派的團隊決定。 但是，Pinterest一般數據科學家角色橫跨實驗和統計建模，基本業務分析和數據可視化，機器學習和深度學習理論 。

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

對另一家擁有大量用戶數據的公司的數據科學感興趣？ 查看“德勤數據科學家訪談”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僅聘請在相關數據科學項目中具有至少3年行業經驗(領導角色至少6年)的合格數據科學家。 聘用要求非常具體，具體取決于團隊的工作角色，因此，這有助于獲得與團隊中的角色保持一致的特定行業經驗。

Other relevant qualifications include:

其他相關資格包括：

• Advanced Degree (MS or PhD) in a quantitative field or related fields.
  定量領域或相關領域的高級學位(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年以上行業經驗(高級職位6年以上)，并具有使用統計方法解決大數據實際問題的可靠記錄。
• Industry experience in both online and offline experimentation.
  在線和離線實驗的行業經驗。
• 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構化和非結構化數據的經驗。
• 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，時間序列，用戶建模，圖像識別，圖形表示學習和自然語言處理等設置中應用深度學習方法的豐富經驗。
• Experience with learning from ranking labels (i.e. triplet learning, metric learning, etc.) and deploying ranking models (i.e. learning-to-rank).
  具有從排名標簽中學習的經驗(即三元組學習，度量學習等)以及部署排名模型(即按等級學習)的經驗。
• Ability to lead initiatives across multiple product areas and communicate findings with leadership and product teams.
  能夠領導多個產品領域的計劃，并與領導和產品團隊交流發現結果。

Pinterest數據科學團隊是什么？ (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數據科學家角色和職能遍布與數據科學相關的眾多團隊和領域。 Pinterest標題為“數據科學家”，涵蓋多個角色和功能，范圍從以產品為重點的分析到更加技術性的機器學習和深度學習功能 。

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

根據指定的團隊，Pinterest數據科學家的職能可能包括：

• 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(離線實驗) ：利用高級數據分析概念來解決涉及離線評估數據的關鍵測量挑戰，從微調測量技術到定義為新產品和現有新產品創建有意義的價值測量方法。

• 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(廣告實驗) ：設計和構建模型，機制和指標，以通過實驗做出合理的產品決策，最終目標是為每個Pinner展示高質量的廣告。

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

  業務運營和策略 ：利用業務分析來推動關鍵業務見解，以更好地了解Pinners，合作伙伴和產品。

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

  廣告質量排名小組 ：應用實驗，定量分析，數據挖掘和數據可視化技術來提高Pinterest上廣告的質量和相關性。

• 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 ：開發機器學習模型，系統和功能，以幫助廣告客戶通過推薦，工具和見解最大化廣告活動在Pinterest上的投資回報。

面試過程 (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.

面試過程從招募人員或招聘經理的初始電話屏幕開始，如果一切順利，將安排與數據科學家或數據工程師的技術屏幕。 通過技術屏幕后，您可以繼續進行現場采訪，其中包括五次背對背的采訪回合，中間有午餐休息時間。

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.

這是與招聘人員進行的30分鐘的初始電話交談，詳細介紹了您的技術背景，您過去的相關項目以及根據履歷快速評估您的技能。 在這次面試中，面試官還將與您討論團隊中的角色和Pinterest文化。

Sample Questions:

樣題：

• Tell me about yourself.
  說說你自己。
• Talk about one of your past work experiences.
  談論您過去的工作經驗之一。

技術畫面 (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.

技術屏幕是對數據科學家進行的一個小時的采訪，討論圍繞過去的項目，您使用的方法以及如何解決某些挑戰進行。

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單SQL編碼。 Pinterest在幾乎所有的技術采訪中都使用“ Karat” ，并且還使用共享屏幕Karat平臺來進行Data Scientist技術篩選。

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“ 采訪查詢 ”中 有關 “ 通過數據科學采訪必須知道的三個SQL概念 ”的文章 ，以為您的采訪做準備。

現場采訪 (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.

現場采訪是Pinterest數據科學家采訪的最后一個采訪階段。 它由五次背對背訪談構成，分為SQL訪談， 統計 和概率訪談，一個編碼訪談和行為訪談。 現場階段的所有采訪都持續約45分鐘，中間有午餐時間。

注意事項 (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數據科學家面試旨在評估候選人設計實驗的能力， 以評估產品性能，大規模建立模型，以及應用數據科學概念來推動增長并提供對業務影響的見解 。 因此，面試問題是標準化的，涵蓋了廣泛的數據科學概念。 掌握統計和概率，假設檢驗，時間序列建模，A / B檢驗，實驗設計，SQL和預測建模概念的知識。

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

通過“ 面試查詢”練習面試問題可以更好地為您做好技術方面的準備。

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系統，為所有人提供了友好的工作環境。 在2019年的一篇文章中，Pinterest被評為“ 硅谷最好的公司 ……這種文化與其他那些積極鼓勵對抗和辯論的高科技公司脫穎而出”。 從文化角度講，Pinterest提供了一個真正進步的工作環境，員工(無論技術與否)都可以成長并蓬勃發展。

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“ LinkedIn數據科學面試問題 ”的指南。

Pinterest數據科學面試問題： (Pinterest Data Science Interview Questions:)

• Give an array of unsorted random numbers (decimals), find the interquartile distance.
  給出一個未排序的隨機數(十進制)數組，找到四分位數距離。
• 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SQL查詢來計算每天從iPhone和Web登錄的唯一身份用戶數，其中iPhone日志和Web日志之間存在明顯的關系。
• Your product manager noticed a dip in a specific metric. How do you go about investigating what may have caused the dip?
  您的產品經理注意到特定指標有所下降。 您如何調查可能導致下降的原因？

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

最初于 2020年8月4日 發布在 https://www.interviewquery.com 。

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

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