You might have noticed that the advice for all the other FANG companies (Facebook, Amazon and Google) was rather similar - learn your data structures and algorithms thoroughly and practice a lot. Netflix will be a bit different (read VERY different). Although the technical requirements are as strenuous as any other FANG companies, Netflix’s culture fit requirements are above everything else for them. Therefore, today we’re going to spend most of the time discussing that.

Let us first go through the engineering part of the discussion.

Netflix doesn’t visit Indian engineering colleges as of now but just like Google and Facebook they keep their social media eyes and ears open for good talent. At Netflix you have a much better chance if you have some prior experience even though there is no indication that talented freshers aren’t welcome.

Just like Google and Facebook, you can get in touch with Netflix recruiters on LinkedIn, Twitter, Facebook and the Netflix Careers page. One difference between Netflix and other FANG companies is that they really do use their careers page for recruitment. Another major difference between them is that Netflix recruits for teams instead of companies. Which means you have to cater your CV to the roles you’re applying for. For software engineering, testing and systems architecture jobs, the advice remains the same. In this issue I will cover how to prepare for the data science and machine learning positions.

Eligibility: An engineering bachelors degree is the minimum threshold and specialization in narrower areas of these fields makes your Cv stronger. Prior experience in relevant positions will make your candidacy attractive to recruiters as at Netflix, they believe in seeing your work. If you can show professional/personal projects that demonstrate your ability to solve their problems you’ll have a much better chance of getting your foot in the door. 

Roles and responsibilities: Netflix has 110 jobs listed under the heading “engineering” and 45 jobs listed under “data”. The fact that about data jobs are about 41% as many as conventional “engineering” jobs should give you an idea how important data science and machine learning are to Netflix.  

Recruitment process: Superficially the process is similar to other web software companies like Google and Facebook but there are some subtle differences.

1. Short phone call: The HR person will call you to take an overview of your career trajectory and career goals. Your previous experience will be discussed. It’s advisable to have used Netflix services before going through this round. It is important that you have a comprehensive knowledge of what Netflix does.
2. 1:1 interview: This part of the process is there to decide your competence for the role. Your technical mastery will play a big role here. A thorough understanding of basic concepts as well as your work on the relevant technologies will be scrutinized. 
3. Group interviews: Here the representatives of the team that you’ll work with not only grill you on your technical expertise but also many difficult cultural, ethical and work/life problems.

How to prepare: As I mentioned before, we’re going to spend most of our time discussing data science, machine learning and AI interview prep in this instalment of FANG series. To begin with you must read these 3 things end to end.

1. Netflix Research Blog: All Articles
2. Deep Learning for Recommender Systems: Talk Slides
3. Reliable ML in the Wild Workshop (ICML 2017): Making ML Reliable at Netflix

In previous posts I have mentioned many other valuable resources for machine learning but these are resources SPECIFICALLY aimed towards machine learning at Netflix. 

Questions to ponder: Some hard questions that have been asked in Netflix interviews are as follows. They require use of data driven thinking for solutions. Work on them. Take some time to really figure out as many variables as possible. It’s less about accuracy and more about whether you can think with data. The Acing AI interview blog lists these question to practice in details before your Netflix machine learning interview.

1. How would you build and test a metric to compare two user’s ranked lists of movie/tv show preferences?How best to select a representative sample of search queries from 5 million?
2. Given a month’s worth of login data from Netflix such as account_id, device_id, and metadata concerning payments, how would you detect fraud? (identity theft, payment fraud, etc.)
3. How would you handle NULLs when querying a data set? Are there any other ways?
4. What is the use of regularization?What are the differences between L1 and L2 regularization, why don’t people use L0.5 regularization for instance?
5. SQL queries to find time difference between two events given a certain condition.
6. Given a single day with a large sample size and a significant test result, would you end the experiment?
7. What do you know about A/B testing in the context of streaming?
8. How do you prevent overfitting and complexity of a model? How do you measure and compare models?
9. How do you know if one algorithm is better than other?
10. Elaborate on the recent project you developed for your company.
11. Why do you use XYZ method? Elaborate on how to improve content optimization?
12. What technology or item that most people feel will be obsolete in the future do you not agree with?
13. Why Rectified Linear Unit is a good activation function?
14. How should we approach attribution modelling to measure marketing effectiveness?
15. How would you determine if the price of a Netflix subscription is truly the deciding factor
16. for a consumer?
17. If Netflix is looking to expand its presence in Asia, what are some factors that you can use to evaluate the size of the Asia market, and what can Netflix do to capture this market?
18. Say the CEO stops by your desk and asks you whether or not we should go into an untapped market. How would you determine the size of the addressable market and the factors the Netflix should consider before deciding to enter the market?

Preferred qualities: At Netflix, interpersonal skills enabling you to be a real value addition to your team are valued the most. So, the biggest chunk of your interview process will center around determining your cultural fit. Read the Netflix culture memo to ensure you don’t put the wrong foot forward. I mean really READ it. A bad cultural fit WILL mean disqualification. It is most definitely not negotiable there. The values they look for in a candidate are:

1. Judgement
2. Communication
3. Curiosity
4. Courage
5. Passion
6. Selflessness
7. Innovation
8. Inclusion
9. Integrity
10. Impact

Netflix has a pretty much ideal mix of freedom and responsibility. They want to create a dream team that will work without supervision. Their managers believe in not providing control but context. They take pride not in how many decisions senior managers have taken but how few decisions they have taken. It means that YOU will be required to take decisions that decide the direction the platform takes. 

Hope you make it to the Netflix dream team. Until next time, happy prepping!