Top 15 LinkedIn Interview Questions

Top 15 LinkedIn Interview QuestionsTop 15 LinkedIn Interview Questions

Overview

LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. The Data Science team, in particular, plays a crucial role in managing complex data systems for various products and driving informed actions. Here’s how LinkedIn conducts its interviews.

LinkedIn Interview Process

LinkedIn’s interview process is engineered to assess a candidate’s technical aptitude, critical thinking, and alignment with LinkedIn’s values and culture. Here’s a breakdown of the stages involved:

  1. Preliminary Screening: This initial step is carried out by LinkedIn’s recruiters who review the candidates’ resumes to ensure they meet the job role’s requirements. A particular emphasis is placed on not just the requisite skills but also a genuine passion for LinkedIn’s mission.
  2. Technical Assessments: Candidates applying for technical positions may be subjected to technical assessments or challenges. This stage is pivotal in evaluating the depth of a candidate’s knowledge in areas pertinent to the job role, such as coding, algorithms, data structures, and other domain-specific topics.
  3. Phone Screening: Following a successful preliminary screening, candidates may undergo a phone screening interview. This interaction helps the recruiters understand the candidates’ career aspirations, motivations, and interest in working at LinkedIn.
  4. On-site or Virtual On-site Interviews: In some cases, candidates may be invited for on-site interviews at LinkedIn’s headquarters or virtual on-site interviews if circumstances dictate. These interviews often involve in-depth discussions with hiring managers and team members to evaluate both cultural fit and technical competence. They may face a mix of technical and behavioral questions and, sometimes, hypothetical business problems to gauge their problem-solving and analytical skills.
  5. Offer and Onboarding: Post-interview, there’s a decision-making phase to determine if there’s a mutual fit. If successful, an offer is extended, and the onboarding process commences, ushering the candidate into LinkedIn’s professional community.

LinkedIn’s interview process is meticulous, with a focus on technical skills, cultural fit, and alignment with the company’s values, ensuring that new hires are well-positioned to contribute to LinkedIn’s mission of connecting the world’s professionals to make them more productive and successful.

LinkedIn SQL Interview Questions

LinkedIn, being a hub of professional connections, hosts a vast amount of data relating to jobs, companies, and individual profiles. The ability to efficiently query, manipulate, and analyze this data is crucial for many roles within the company.

Database management and SQL proficiency are key skills that LinkedIn looks for in candidates, especially those applying for data-centric positions. Here are a few questions to test your database knowledge in a LinkedIn context:

1. Write a query to forecast the budget for all projects and label them as “over budget” or “within budget”.

”Over budget” on a project is defined as when the salaries, prorated to the day, exceed the budget of the project. For example, if Alice and Bob both combined income make 200K and work on a project with a budget of 50K that takes half a year, then the project is over budget given 0.5 * 200K = 100K > 50K. Write a query to forecast the budget for all projects and return a label of “over budget” if it is over budget and “within budget” otherwise. Note: Assume that employees only work on one project at a time.

2. Create a function to calculate the month-over-month change in revenue for the year 2019.

Given a table of transactions and products, write a function to get the month_over_month change in revenue for the year 2019. Make sure to round month_over_month to 2 decimal places.

3. Write a query to retrieve the number of users that have posted each job only once and multiple times.

Given a table of job postings, write a query to retrieve the number of users that have posted each job only once and the number of users that have posted at least one job multiple times. Each user has at least one job posting. Thus, the sum of single_post and multiple_posts should equal the total number of distinct user_id’s.

To further enhance your knowledge of Databases, consider exploring the SQL learning path and the comprehensive list of SQL questions and solutions in our interview questions database.

LinkedIn Coding and Algorithms Interview Questions

With a user base of hundreds of millions, LinkedIn faces complex challenges that require robust algorithmic solutions. The platform’s functionality, ranging from connection suggestions to job recommendations, relies heavily on efficient algorithms and adept coding skills.

In interviews, LinkedIn often assesses a candidate’s problem-solving ability through coding and algorithm questions, making them a critical aspect of the preparation.

4. Write a function to generate all possible combinations of dice rolls.

You are given n dice, each with m faces. Your task is to write a function combinational_dice_rolls that generates all possible combinations of dice rolls. Can you do it recursively?

5. Write an SQL query to retrieve the number of users that have posted each job only once and multiple times.

You have a job_postings table with job postings data. Write a query to find the number of users that have posted each job only once and the number of users that have posted at least one job multiple times.

6. Write a function to select a random number from a stream with equal probability.

Given a stream of numbers, your task is to write a function random_number that selects a random number from the stream with equal probability and $O(1)$ space in selection.

To practice Coding and Algorithms interview questions, consider using the Python learning path or the full list of Coding and Algorithms questions in our database.

LinkedIn Machine Learning Case Study Interviews

LinkedIn utilizes machine learning to enhance user experience by personalizing content, job recommendations, and networking opportunities.

With a vast amount of data from user profiles, job postings, and company pages, LinkedIn provides a fertile ground for implementing and improving machine learning models. As such, understanding machine learning concepts and being able to apply them to real-world problems is vital for candidates aspiring to join LinkedIn’s technical teams.

7. What is the difference between Lasso and Ridge Regression?

Explain the differences between Lasso and Ridge Regression.

8. How would you provide reasons for loan rejection without access to feature weights?

Suppose you have a binary classification model that determines loan eligibility. As a financial company, you must provide each rejected applicant with a reason for their rejection. However, you don’t have access to the feature weights. How would you explain the rejection to each applicant?

9. How would you build a job recommendation feed using user LinkedIn profiles, job applications, and user responses?

You’re working on a job recommendation engine and have access to all user LinkedIn profiles, a list of jobs each user applied to, and answers to questions that the user filled in about their job search. How would you use this information to build a job recommendation feed?

To get ready for machine learning interview questions, we recommend taking the machine learning course.

LinkedIn Analytics and Metrics Case Study Interviews

Data-driven decision-making is at the core of LinkedIn’s continuous improvement. The platform thrives on its ability to analyze user behavior, market trends, and the effectiveness of new features.

Candidates with a strong background in analytics, statistics, and experimental design are well-positioned to contribute to LinkedIn’s mission of creating economic opportunities for every member of the global workforce.

10. Why is the number of job applicants decreasing despite steady job postings?

You’re analyzing a job board’s metrics and notice that while the number of job postings per day has remained the same over the past few months, the number of applicants has been steadily decreasing. What could be causing this trend?

11. What are type I and type II errors in hypothesis testing, and how do they differ?

In the context of hypothesis testing, define type I and type II errors and explain the difference between them. As a bonus, describe the probability of making each type of error mathematically.

12. How would you analyze the performance of a new feature without AB testing?

You’re a data scientist at LinkedIn working on a product that sends qualified job candidates to companies. The team has launched a new feature that allows candidates to message hiring managers directly during the interview process. Due to engineering constraints, the feature couldn’t be AB tested before launch. How would you analyze its performance?

For practicing Analytics and Experiments questions, consider using the product metrics learning path and the data analytics learning path. These resources can help you understand the key concepts and prepare effectively.

LinkedIn Statistics and Probability Interview Questions

The vast amount of data generated on LinkedIn daily presents ample opportunity for statistical analysis to drive informed decisions and create better user experiences.

Understanding the principles of statistics and probability is crucial for roles that involve data analysis, A/B testing, and machine learning. Statistics and probability questions are often asked during interviews to gauge a candidate’s ability to derive meaningful insights from data and make evidence-based recommendations.

13. What are type I and type II errors in hypothesis testing, and how do they differ?

In the context of hypothesis testing, explain what type I and type II errors are and how they differ. As a bonus, describe the probability of making each type of error mathematically.

14. What’s the probability of drawing three cards in ascending order from a shuffled deck of 500?

Imagine a deck of 500 cards numbered from 1 to 500. If all the cards are shuffled randomly, and you are asked to pick three cards, one at a time, what’s the probability of each subsequent card being larger than the previously drawn card?

15. What is the probability it’s raining in Seattle based on the responses of three friends?

You are about to get on a plane to Seattle and want to know if you should bring an umbrella. You call 3 random friends who live there and ask each independently if it’s raining. Each friend has a 23 chance of telling you the truth and a 13 chance of lying. All 3 friends tell you that “Yes” it is raining. What is the probability that it’s actually raining in Seattle?

For mastering Statistics and Probability, consider the Statistics and A/B testing learning path and the Probability learning path. These resources will provide you with a comprehensive understanding of the subject.

LinkedIn Interview Questions

Practice for the LinkedIn interview with these recently asked interview questions.

Question
Topics
Difficulty
Ask Chance
Python
Algorithms
Hard
Very High
Machine Learning
Medium
High
SQL
Analytics
Hard
High

View all Linkedin questions

LinkedIn Salaries by Position

$132K
$250K
Product Manager
Median: $190K
Mean (Average): $194K
Data points: 115
$66K
$250K
Software Engineer
Median: $165K
Mean (Average): $166K
Data points: 1,186
$60K
$206K
Machine Learning Engineer
Median: $165K
Mean (Average): $156K
Data points: 17
$98K
$193K
Growth Marketing Analyst
Median: $135K
Mean (Average): $143K
Data points: 12
$106K
$200K
Data Scientist
Median: $126K
Mean (Average): $137K
Data points: 251
$86K
$173K
Business Analyst
Median: $145K
Mean (Average): $137K
Data points: 26
$86K
$186K
Data Engineer
Median: $128K
Mean (Average): $132K
Data points: 15
$106K
$146K
Data Analyst
Median: $106K
Mean (Average): $121K
Data points: 3
$77K
$108K
Business Intelligence
Median: $98K
Mean (Average): $95K
Data points: 4

Most data science positions fall under different position titles depending on the actual role.

From the graph we can see that on average the Product Manager role pays the most with a $193,974 base salary while the Business Intelligence role on average pays the least with a $94,621 base salary.

Related Content

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