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’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:
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, 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:
”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.
Given a table of
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.
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
multiple_posts should equal the total number of distinct
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.
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?
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.
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.
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.
Explain the differences between Lasso and Ridge Regression.
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?
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?
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.
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?
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.
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.
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.
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.
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?
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 2⁄3 chance of telling you the truth and a 1⁄3 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.
Practice for the LinkedIn interview with these recently asked interview questions.
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