介绍 (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.是一家社交媒体网络和移动应用程序公司，成立于2009年，总部位于加利福尼亚州旧金山。 该公司开发和运营软件应用程序和系统，旨在使用图像，GIF和视频(称为Pins)在线发现和保存信息。 它提供免费注册，之后允许用户通过称为插脚板的图像库上载，保存，分类和管理图像及其他内容，例如视频(图钉)。

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”.

自成立以来，它的平均用户数量一直稳定增长，因为观众经常转向该平台，以“规划社交活动，购物，通过how-to帖子学习事物，并通过视觉规划板块的生活时刻”。

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活跃平均月度用户在全球范围内已超过3.35亿，其中超过1,300亿个项目固定在超过30亿个虚拟固定板上。 有了这些信息，可以想象每天产生的大量数据并非难事。 数据科学是Pinterest产品和服务的核心，Pinterest数据科学家利用最先进的分析工具和机器学习模型来利用这些数据来指导业务决策。

Pinterest数据科学角色 (The Data Science Role at Pinterest)

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

即使到现在，Pinterest仍是一家成长中的公司，拥有许多团队和部门致力于关键功能，产品和服务，以改善客户体验。

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数据科学团队有时会与其他团队合作，围绕几乎所有面向用户的功能设计实验 ，以帮助理解每天生成的庞大客户数据，从而推动决策制定并提供对业务有影响的见解 。 因此，Pinterest数据科学家角色很大程度上由指派的团队决定。 但是，Pinterest一般数据科学家角色横跨实验和统计建模，基本业务分析和数据可视化，机器学习和深度学习理论 。

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

对另一家拥有大量用户数据的公司的数据科学感兴趣？ 查看“德勤数据科学家访谈”文章！

必备技能 (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仅聘请在相关数据科学项目中具有至少3年行业经验(领导角色至少6年)的合格数据科学家。 聘用要求非常具体，具体取决于团队的工作角色，因此，这有助于获得与团队中的角色保持一致的特定行业经验。

Other relevant qualifications include:

其他相关资格包括：

• Advanced Degree (MS or PhD) in a quantitative field or related fields.定量领域或相关领域的高级学位(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年以上行业经验(高级职位6年以上)，并具有使用统计方法解决大数据实际问题的可靠记录。
• Industry experience in both online and offline experimentation.在线和离线实验的行业经验。
• 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等软件包管理和分析结构化和非结构化数据的经验。
• 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.在推荐系统，时间序列，用户建模，图像识别，图形表示学习和自然语言处理等设置中应用深度学习方法的丰富经验。
• Experience with learning from ranking labels (i.e. triplet learning, metric learning, etc.) and deploying ranking models (i.e. learning-to-rank).具有从排名标签中学习的经验(即三元组学习，度量学习等)以及部署排名模型(即按等级学习)的经验。
• Ability to lead initiatives across multiple product areas and communicate findings with leadership and product teams.能够领导多个产品领域的计划，并与领导和产品团队交流发现结果。

Pinterest数据科学团队是什么？ (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数据科学家角色和职能遍布与数据科学相关的众多团队和领域。 Pinterest标题为“数据科学家”，涵盖多个角色和功能，范围从以产品为重点的分析到更加技术性的机器学习和深度学习功能 。

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

根据指定的团队，Pinterest数据科学家的职能可能包括：

• 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(离线实验) ：利用高级数据分析概念来解决涉及离线评估数据的关键测量挑战，从微调测量技术到定义为新产品和现有新产品创建有意义的价值测量方法。

• 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(广告实验) ：设计和构建模型，机制和指标，以通过实验做出合理的产品决策，最终目标是为每个Pinner展示高质量的广告。

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

  业务运营和策略 ：利用业务分析来推动关键业务见解，以更好地了解Pinners，合作伙伴和产品。

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

  广告质量排名小组 ：应用实验，定量分析，数据挖掘和数据可视化技术来提高Pinterest上广告的质量和相关性。

• 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.

  广告智能 ：开发机器学习模型，系统和功能，以帮助广告客户通过推荐，工具和见解最大化广告活动在Pinterest上的投资回报。

面试过程 (The Interview Process)

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.

面试过程从招募人员或招聘经理的初始电话屏幕开始，如果一切顺利，将安排与数据科学家或数据工程师的技术屏幕。 通过技术屏幕后，您可以继续进行现场采访，其中包括五次背对背的采访回合，中间有午餐休息时间。

初始画面 (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.

这是与招聘人员进行的30分钟的初始电话交谈，详细介绍了您的技术背景，您过去的相关项目以及根据履历快速评估您的技能。 在这次面试中，面试官还将与您讨论团队中的角色和Pinterest文化。

Sample Questions:

样题：

• Tell me about yourself.说说你自己。
• Talk about one of your past work experiences.谈论您过去的工作经验之一。

技术画面 (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.

技术屏幕是对数据科学家进行的一个小时的采访，讨论围绕过去的项目，您使用的方法以及如何解决某些挑战进行。

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.

在这次采访中还将有一些简单SQL编码。 Pinterest在几乎所有的技术采访中都使用“ Karat” ，并且还使用共享屏幕Karat平台来进行Data Scientist技术筛选。

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.

我们至少建议您 阅读有关“ 采访查询 ”中 有关 “ 通过数据科学采访必须知道的三个SQL概念 ”的文章 ，以为您的采访做准备。

现场采访 (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.

现场采访是Pinterest数据科学家采访的最后一个采访阶段。 它由五次背对背访谈构成，分为SQL访谈， 统计 和概率访谈，一个编码访谈和行为访谈。 现场阶段的所有采访都持续约45分钟，中间有午餐时间。

注意事项 (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数据科学家面试旨在评估候选人设计实验的能力， 以评估产品性能，大规模建立模型，以及应用数据科学概念来推动增长并提供对业务影响的见解 。 因此，面试问题是标准化的，涵盖了广泛的数据科学概念。 掌握统计和概率，假设检验，时间序列建模，A / B检验，实验设计，SQL和预测建模概念的知识。

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

通过“ 面试查询”练习面试问题可以更好地为您做好技术方面的准备。

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拥有以员工为中心的生态系统，为所有人提供了友好的工作环境。 在2019年的一篇文章中，Pinterest被评为“ 硅谷最好的公司 ……这种文化与其他那些积极鼓励对抗和辩论的高科技公司脱颖而出”。 从文化角度讲，Pinterest提供了一个真正进步的工作环境，员工(无论技术与否)都可以成长并蓬勃发展。

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

拥有良好工作文化的另一家公司是LinkedIn。 查阅有关“ LinkedIn数据科学面试问题 ”的指南。

Pinterest数据科学面试问题： (Pinterest Data Science Interview Questions:)

• Give an array of unsorted random numbers (decimals), find the interquartile distance.给出一个未排序的随机数(十进制)数组，找到四分位数距离。
• 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.编写一个SQL查询来计算每天从iPhone和Web登录的唯一身份用户数，其中iPhone日志和Web日志之间存在明显的关系。
• Your product manager noticed a dip in a specific metric. How do you go about investigating what may have caused the dip?您的产品经理注意到特定指标有所下降。 您如何调查可能导致下降的原因？

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

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