DA/DS 求职刷题指南(上)- 含内推机会


Data Scientist/Data Analyst 通常需要集中准备的分为以下几块内容:

  • Machine Learning
  • 统计,概率与 A/B testing
  • Online coding(Python + R)
  • SQL
  • Product sense
  • Project
  • Extra Skills

一、 MachineLearning

  1. 常见面试问题
  • What is overfitting? / Please briefly describe what is bias vs. variance.
  • How do you overcome overfitting? Please list 3-5 practical experience. / What is ‘Dimension Curse’? How to prevent?
  • Please briefly describe the Random Forest classifier. How did it work? Any pros and cons in practical implementation?
  • Please describe the difference between GBM tree model and Random Forest.
  • What is SVM? what parameters you will need to tune during model training? How is different kernel changing the classification result?
  • Briefly rephrase PCA in your own way. How does it work? And tell some goods and bads about it.
  • Why doesn’t logistic regression use R^2?
  • When will you use L1 regularization compared to L2?
  • List out at least 4 metrics you will use to evaluate model performance and tell the advantage for each of them. (F1 score, ROC curve, recall, etc…)
  • What would you do if you have > 30% missing value in an important field before building the model?
  1. 相关资料准备
  • Coursera 上 Andrew Ng 的 Machine learning 课程: https://www.coursera.org/learn/machine-learning 算得上考古级别的课程了,内容有些老旧但是很经典,很适合商学院 BA 专业的从 0 开始补齐 ML 的背景知识
  • 【15 hours of expert ML videos】: https://www.dataschool.io/15- hours-of-expert-machine-learning-videos/
  • 《ISLR》(一个免费链接直通车),入门神书
  • Practical Statistics for Data Scientists: 50 Essential Concepts》,很实用的一本书, 专讲一些细小知识,不深但是读完会感觉多了些对 ML 的理解。
  • Medium-Towards Data Science 专题,比如 Machine Learning 101 (Machine Learning 101 – Medium)这个小专题,非常浅显易懂,适合初学者用具象的方式理解抽象算法
  • StackOverflow(https://stackoverflow.com/)自然也是不能漏掉的,学 data 或者编程总会遇到很细枝末节的问题,这些一般文章里没有,所以就需要求助社群的力量了。
  • DataCamp:Machine Learning A-Zhttps://lnkd.in/gXqdBsQ

二、统计,概率与A/B Testing

  1. 常见面试问题
  • What is p-value? What is confidence interval? Explain them to a product manager or non-technical person… (很明显人家不想让你回答: 画个正态分布然后两边各卡 5%
  • How do you understand the “Power” of a statistical test?
  • If a distribution is right-skewed, what’s the relationship between medium, mode, and mean?
  • When do you use T-test instead of Z-test? List some differences between these two.
  • Dice problem-1: How will you test if a coin is fair or not? How will you design the process(有时会要求编程实现)? what test would you use?
  • Dice problem-2: How to simulate a fair coin with one unfair coin?
  • 3 door questions. (自行 google 吧,经典题之一)
  • Bayes Questions: Tom takes a cancer test and the test is advertised as being 99% accurate: if you have cancer you will test positive 99% of the time, and if you don’t have cancer, you will test negative 99% of the time. If 1% of all people have cancer and Tom tests positive, what is the prob that Tom has the disease? (非常经典的 cancer screen 的题,做会这一道,其他都没问题了)
  • How do you calculate the sample size for an A/B testing?
  • If after running an A/B testing you find the fact that the desired metric(i.e, Click Through Rate) is going up while another metric is decreasing(i.e., Clicks). How would you make a decision?
  • Now assuming you have an A/B testing result reflecting your test result is kind of negative (i.e, p-value ~= 20%). How will you communicate with the product manager?
  • If given the above 20% p-value, the product manager still decides to launch this new feature, how would you claim your suggestions and alerts?
  1. 相关资料准备
  • A/B testing 的资料首推的是 Udacity 上免费的 A/B testing(by Google)的课, 同学们的评 价都还不错,很适合全面的了解一下 A/Btesting。
  • 其余的 A/B testing 的内容大多来自于 Medium 上的好文,原因是 A/B testing 是一个 要和实际的业界应用场景结合的东西,只知道原理和基本不懂没啥区别。所以要去看 一看业界的人写的关于 A/B testing 的文章,只 da 有带着案例看,才能懂面试中的问题都应该怎么样回答。
  • 还有就是如果有在工作的学长姐,长辈等等,一定要不吝啬的问 A/B 方面的问题。他们说个十几二十分钟,能省下你很多时间去到处扒资料,原因同上条不解释。
  • Stats 的话,有一个非常快的捡起一些统计学基础的内容是 Coursera 上 intro to stats and prob 课程,很快,一个下午就可以看完。
  • Udemy 课程:Data Science Career Guide - Interview Preparation, 还是很不错的。课 程轻量,学起来无压力。
  • 概率题对于大多数中国学生来说都没问题,都是高中学过的,稍加捡起就行。Udemy 的课就可以帮你捡起来

三、Online coding (Python+R)

  1. 面试问题(这个考的五花八门,所以不敢说是最常见的)
  • Report the biggest sum of a continuous 3 numbers in a list? with the related index?
  • Dynamic programming problem: Now you have 5 types of coins(1,2,3,5,8) and a total sum(a big number, say 589). How many different combinations of coins can you find to reach this total sum?
  • Please write a function to reverse the key and value in a dictionary. When you have repeated values, please only keep the first key as the new value.
  • Similarly to the “gather” and “spread” functions in the tidyr package, write a one by yourself and test it using XXX dataset.
  • Given a log file with rows featuring a date, a number, and then a string of names, parse the log file and return the count of unique names aggregated by month. (我的不是这个原题,但是意思很像)
  • Using python to calculate a 30-day rolling profit. (大致就是要用 python 写一个 rolling window)
  1. 相关资料准备


[现有内推机会 - New Grads Friendly!]

1. 硅谷南湾智能电动汽车"EV"公司。设计,开发,制造和销售与先进的互联网,人工智能和自动驾驶技术无缝集成的智能电动汽车。致力于内部研发和智能制造,以为客户创造更好的出行体验。致力于通过技术和数据改造智能电动汽车,塑造未来的出行体验

招聘 Entry Level [Data Platform Engineer] | [Machine Learning Infrastructure Engineer]

全职起薪 $75000,Sponsor OPT/Ext/H1b

2. 南加州Banking App研发商,致力于创造可增强美国集体潜力的金融机会。其金融工具,包括借记卡和支出帐户,可帮助超过800万客户进行银行业务,制定预算,避免透支费用,找到工作并建立信贷。合作方包含Mark Cuban,Norwest Venture Partners,Section 32和Financial Venture Studios等。

招聘 Entry Level [Data Engineer]

全职起薪 $70000,Sponsor OPT/Ext/H1b

3. 为合作商家打造的一站式SAAS平台,包含Instagram,YouTube, Tiktok等数百万网红资源,是品牌扩大知名度最有效的工具。通过人工智能,大数据分析,和丰富的网红营销经验,为数以百计的品牌量身定制专属网红营销方案。

招聘 Entry Level [Data Analyst]

全职起薪 $72000,Sponsor OPT/Ext/H1b/GC

[Job Descriptions/Requirements]

Data Engineer

  • Provide seamless and timely data access for your users;
  • Build reliable and dependable ETL;
  • Build and maintain production machine learning infrastructure;
  • Troubleshoot complex issues in distributed systems;
  • Debate data processing philosophies and methodologies with your team;
  • Familiar with Python, Java, SQL

Machine Learning Engineer

  • Profile large-scale training jobs and identify/resolve bottlenecks;
  • Increase training speed by mixed-precision, faster database design and preprocess optimization;
  • Work with Infra to build hyper-parameter tuning pipeline and experiments database;
  • Work with Infra to build release pipeline that include model-pruning, model to car release and writing GPU.

Data Analyst

  • Assist product manager to deal with daily product development, delivery, and client communication duties
  • Conduct research on different business issues in a group based on data in Google Analytics
  • Work in a group to improve new product marketing copy-writing for used as an introduction and new products directly to customers
  • Put together pages of competitive product analysis independently through collecting and analyzing required information from the finical annual report and official website and presented reports on the weekly meetings
  • Work in Business Processing Re-Engineering group to remove redundancy and optimize current sales, marketing processes by using ARIS Express
  • Prepare Dashboards using calculations, parameters, calculated fields, groups, sets, and hierarchies in Tableau
  • Publish Tableau dashboard on Tableau Server or Tableau Online and embedded them into the portal.