Recently, many students will put it into consideration when applying for DS jobs at major North American manufacturers. LinkedIn Put it on the target list, but anyone who has actually met it knows that the screening intensity of this company is not inferior to FAANG. The overall process usually lasts 4-5 rounds, from product understanding to technical depth to value matching. Almost every step is to verify whether you are a data scientist who understands the business and can implement it.
Let’s break down the complete process and the key points of the actual investigation to give students who are preparing to submit LinkedIn NG Data Scientist a clearer expectation.
Recruiter Screen
The first round is usually a 30 minute Recruiter call. Many people will mistakenly think that this is a process, but in fact people are already being screened here. In addition to the regular background introduction and job motivation, they will also ask questions around the LinkedIn product itself, such as what do you think are the most important metrics of LinkedIn, how to understand its business model, and what is the core value of the platform.
The essence of this round is to see if you have product understanding. Many candidates will directly say that it is a platform for finding jobs, but in the eyes of interviewers, LinkedIn is more like a professional identity network, with both B2B and B2C two-wheel drive attributes. Revenue comes from recruitment, advertising and subscriptions, and it has a strong network effect. If the understanding remains at the surface level, it is easy to stop at the first round.
It is recommended to think clearly in advance about the North Star Metric of the platform, how supply and demand match, and why users will stay here for a long time. What the interviewer expects to see more is in-depth thinking rather than functional description.
Hiring Manager Screen
This round is often underestimated, but is actually so critical that many Hiring Managers make the direct decision here whether to move forward with the process. The interview format is usually a combination of Product Case and Analytics Case. For example, if you want to increase the content interaction rate, how would you design an A/B Test; if DAU continues to decline after a new feature is launched, how would you unravel the reason; or how would you judge whether a feature really creates user value.
What is tested here is not complex formulas, but structured thinking ability. A mature answer usually first clarifies the target indicators, then breaks down the influencing factors, then designs experiments and verifies the results, and finally gives implementable business suggestions. The interviewer hopes to see that you can understand users and scenarios like a product manager, and use data to make judgments like a data scientist.
Just modeling is not enough at LinkedIn. They value more whether you have business sensitivity and whether you can turn analysis results into decision-making basis.
Technical Interviews
The technical aspect usually has 3–4 rounds, led by Data Scientist or Engineer, covering multiple directions such as SQL, Python, Statistics, A/B Testing and Machine Learning. The overall difficulty of the questions is above average, but the real pressure comes from the time limit - the pace is very fast, and you need to accurately complete the analysis and expression within the limited time.
LinkedIn's technical side has a very obvious feature: it is obviously biased towards business scenarios rather than pure algorithms. For example, they may not let you brush a particularly difficult LeetCode, but they will ask in-depth questions about how to detect experimental contamination, how to deal with sample imbalance, and whether indicator fluctuations really exist. This type of problem is closer to the real work environment and requires you to have judgment skills, not just problem-solving skills.
It’s not that many candidates don’t know how to do it, but they just haven’t adapted to this rhythm of high-density communication and rapid reasoning. Practicing in advance using the "think and speak" method will significantly improve your performance.
Final Round
The final round usually involves a conversation with the Senior Leadership or Senior Manager. Technology is no longer the focus, the other party is more concerned about your teamwork experience, how to deal with conflicts, career plans, and why you choose LinkedIn.
The company places great emphasis on the concept of “Members First”, which means that all decisions should be centered around creating long-term value for users. If your answer only emphasizes growth, monetization or efficiency and ignores user experience, you will often not receive points. They prefer to see long-termism, empathy, a sense of collaboration, and an attitude of responsibility for results.
In other words, they are not only looking for people with matching abilities, but also long-term builders with consistent values.
More interview support
We have compiled a large number of first-line interview and real test variations here, covering the common examination range from NG to Senior. If you want to get more specific questions and ideas, or want to know about our Interview assistance Interview assistance
