Samsung Electronics Data Scientist Interview Questions + Guide in 2025

Samsung Electronics is a global leader in mobile technologies and consumer electronics, committed to pushing the boundaries of innovation to provide groundbreaking connected experiences.

As a Data Scientist at Samsung, you will play a crucial role in transforming complex datasets into actionable insights that drive business decisions. Your responsibilities will include deploying machine learning models, developing analytics tools, and collaborating closely with cross-functional teams to extract meaningful insights from data. You are expected to have strong technical expertise in machine learning, data engineering, and statistical analysis, along with proficiency in programming languages such as Python and SQL. Key skills also include a solid understanding of big data frameworks like Hive and Spark, as well as experience with data visualization techniques.

At Samsung, a great Data Scientist is not only technically adept but also possesses strong communication and interpersonal skills, enabling you to work effectively in a collaborative environment. You should be able to manage multiple projects, prioritize tasks, and meet deadlines while maintaining a strong attention to detail. Your passion for innovation and a proactive approach to problem-solving will be essential as you contribute to the company’s mission of delivering exceptional customer experiences through data-driven strategies.

This guide will equip you with the necessary insights to prepare for your interview, helping you highlight your technical skills and alignment with Samsung's values and objectives.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Samsung Electronics. The interview process will likely assess your technical expertise in data science, machine learning, and your ability to communicate complex ideas effectively. Be prepared to demonstrate your problem-solving skills and your experience with large-scale data systems.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial.

How to Answer

Discuss the definitions of both types of learning, providing examples of algorithms used in each. Highlight scenarios where each type is applicable.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as using regression for predicting house prices. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customers based on purchasing behavior.”

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Detail the project scope, your role, the techniques used, and the challenges encountered, along with how you overcame them.

Example

“I worked on a recommendation system for an e-commerce platform. One challenge was dealing with sparse data. I implemented collaborative filtering and enhanced it with content-based filtering to improve recommendations, which increased user engagement by 20%.”

3. How do you handle overfitting in a model?

This question tests your understanding of model evaluation and optimization.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning that can help mitigate overfitting.

Example

“To prevent overfitting, I use cross-validation to ensure the model generalizes well to unseen data. Additionally, I apply regularization techniques like L1 and L2 to penalize overly complex models, which helps maintain a balance between bias and variance.”

4. What is the purpose of feature engineering, and can you provide an example?

Feature engineering is critical in improving model performance.

How to Answer

Explain the concept and importance of feature engineering, and provide a specific example from your experience.

Example

“Feature engineering involves creating new input features from existing data to improve model performance. For instance, in a sales prediction model, I derived features like ‘days since last purchase’ and ‘average order value’ to enhance the model’s predictive power.”

5. Explain how you would approach building a recommender system.

This question evaluates your understanding of practical applications of machine learning.

How to Answer

Outline the steps involved in building a recommender system, including data collection, model selection, and evaluation metrics.

Example

“I would start by gathering user interaction data, then choose between collaborative filtering and content-based filtering based on the data available. After building the model, I would evaluate its performance using metrics like precision and recall, and iterate based on user feedback.”

Statistics & Probability

1. What is the Central Limit Theorem, and why is it important?

This question tests your foundational knowledge in statistics.

How to Answer

Define the Central Limit Theorem and explain its significance in statistical inference.

Example

“The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for making inferences about population parameters using sample statistics.”

2. How do you interpret a p-value?

Understanding hypothesis testing is essential for data analysis.

How to Answer

Explain what a p-value represents in the context of hypothesis testing.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating a statistically significant result.”

3. Can you explain the difference between Type I and Type II errors?

This question assesses your understanding of statistical errors.

How to Answer

Define both types of errors and provide examples of each.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, in a medical trial, a Type I error might mean concluding a drug is effective when it is not, while a Type II error would mean missing a truly effective drug.”

4. What is a confidence interval, and how do you calculate it?

This question tests your knowledge of statistical estimation.

How to Answer

Define a confidence interval and explain how to calculate it using sample data.

Example

“A confidence interval provides a range of values within which we expect the population parameter to lie, with a certain level of confidence. It is calculated using the sample mean, the standard error, and a critical value from the t-distribution or z-distribution, depending on the sample size.”

5. How would you assess the correlation between two variables?

This question evaluates your ability to analyze relationships in data.

How to Answer

Discuss methods for assessing correlation, such as Pearson’s correlation coefficient, and the implications of the results.

Example

“I would calculate Pearson’s correlation coefficient to measure the linear relationship between two variables. A coefficient close to 1 or -1 indicates a strong correlation, while a value near 0 suggests no correlation. I would also visualize the relationship using a scatter plot for better insight.”

Here are some tips to help you excel in your interview.

Prepare for a Rigorous Technical Assessment

Samsung's interview process is known for its intensity, particularly in technical assessments. Be ready to tackle complex problems related to data analysis, machine learning, and programming. Brush up on your knowledge of probabilities and statistical methods, as these are frequently tested. Familiarize yourself with common data science techniques and algorithms, and practice coding challenges on platforms like LeetCode or HackerRank to sharpen your skills.

Showcase Your Hands-On Experience

Samsung values candidates who can demonstrate practical experience in data science. Be prepared to discuss specific projects where you applied machine learning techniques, built data pipelines, or developed analytics tools. Highlight your role in these projects, the challenges you faced, and the impact of your work on the business. This will not only showcase your technical skills but also your ability to deliver results in a fast-paced environment.

Emphasize Collaboration and Communication Skills

Given the collaborative nature of the role, it's essential to demonstrate your ability to work effectively with cross-functional teams. Be ready to discuss how you've successfully partnered with stakeholders, communicated complex technical concepts to non-technical audiences, and contributed to team dynamics. Samsung looks for candidates who can not only excel in their technical domain but also foster a positive team environment.

Understand the Company Culture

Samsung promotes a culture of innovation and inclusivity. Familiarize yourself with the company's values and recent initiatives, especially those related to sustainability and technological advancements. During the interview, express your alignment with these values and how you can contribute to fostering an inclusive and innovative workplace. This will help you stand out as a candidate who is not only technically proficient but also culturally fit.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities, adaptability, and leadership skills. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on past experiences where you demonstrated resilience, creativity, or leadership, especially in challenging situations. This will help you convey your thought process and decision-making skills effectively.

Stay Updated on Industry Trends

As a data scientist, it's crucial to stay informed about the latest trends and technologies in the field. Be prepared to discuss how emerging technologies, such as AI and big data analytics, can impact Samsung's business. This not only shows your passion for the field but also your proactive approach to continuous learning and improvement.

Follow Up Professionally

After the interview, send a thoughtful thank-you email to your interviewers. Express your appreciation for the opportunity to interview and reiterate your enthusiasm for the role. This small gesture can leave a lasting impression and demonstrate your professionalism.

By following these tips, you'll be well-prepared to navigate the interview process at Samsung Electronics and showcase your qualifications as a data scientist. Good luck!

The interview process for a Data Scientist role at Samsung Electronics is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and potential contributions to the team.

1. Resume Screening

The process begins with a thorough resume screening conducted by the HR team. Candidates are evaluated based on their educational background, relevant work experience, and technical skills. This initial step ensures that only those who meet the basic qualifications are invited to proceed to the next stage.

2. Initial Phone Interview

Following the resume screening, candidates typically participate in a phone interview with an HR representative. This conversation focuses on the candidate's previous experiences, motivations for applying, and understanding of the role. It may also include some preliminary technical questions to gauge the candidate's foundational knowledge in data science and analytics.

3. Technical Assessment

Candidates who successfully pass the initial phone interview are often required to complete a technical assessment. This may involve a written test or a coding challenge that evaluates proficiency in programming languages such as Python, SQL, or R, as well as knowledge of machine learning concepts and algorithms. The assessment is designed to test the candidate's ability to solve real-world data problems and demonstrate their analytical skills.

4. Group Interview

The next stage usually involves a group interview, where candidates meet with multiple team members, including data scientists and managers. This format allows interviewers to assess how candidates collaborate and communicate with others. Candidates may be asked to discuss their past projects, share insights on data analysis techniques, and solve problems collaboratively.

5. Onsite Interview

Candidates who excel in the group interview are typically invited for an onsite interview. This stage consists of several one-on-one interviews with senior data scientists and executives. Interviewers will delve deeper into technical topics, including statistical methods, machine learning frameworks, and data engineering practices. Candidates may also be asked to present their previous work or case studies, showcasing their problem-solving abilities and technical expertise.

6. Final Interview with Leadership

The final stage often includes a discussion with senior leadership or department heads. This interview focuses on the candidate's vision for the role, alignment with Samsung's goals, and ability to contribute to the company's innovative projects. Candidates may also discuss their long-term career aspirations and how they see themselves fitting into the organization.

7. HR Discussion

The last step in the interview process is typically a conversation with HR regarding salary expectations, benefits, and any other logistical details related to the job offer. This is also an opportunity for candidates to ask any remaining questions they may have about the company culture or the role itself.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may arise during each stage of the process.

1. How would you build a model to predict which merchants DoorDash should acquire in a new market?

As a data scientist at DoorDash, describe the steps and features you would use to build a predictive model for identifying potential merchants for acquisition when entering a new market.

2. How would you determine customer service quality through the chat box for small businesses on Facebook Marketplace?

Your team at Facebook focuses on helping small businesses increase sales through the Marketplace app. Explain how you would assess the quality of customer service interactions via the chat box for these businesses.

3. What business health metrics would you track on a dashboard for an e-commerce D2C sock business?

As the person in charge of an e-commerce D2C business selling socks, list and explain the key business health metrics you would monitor on a company dashboard.

4. Write a query to determine if user interactions (likes, comments) lead to higher purchasing volumes.

Given three tables (users, transactions, and events), write a SQL query to analyze whether users who interact on the website (e.g., likes, comments) have a higher purchasing volume than those who do not interact.

5. Write a query to get the average order value by gender.

Given three tables representing customer transactions and customer attributes, write a query to get the average order value by gender. We’re looking at the average order value of users who have ever placed an order. Round your answer to two decimal places.

6. Write a function missing_number to find the missing number in an array.

You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. The complexity should be (O(n)).

7. Write a function to find the index where the sum of the left half equals the right half.

Given a list of integers, find the index at which the sum of the left half of the list is equal to the right half. If no index exists where this condition is satisfied, return -1.

8. Write a function sorting to sort a list of strings in ascending order from scratch.

Given a list of strings, write a function, sorting to sort the list in ascending alphabetical order. Do not use the built-in sorted function. Return the new sorted list rather than modify the list in place. Aim for a solution with (O(n \log n)) complexity.

9. Write a query to extract the earliest date each user played their third unique song.

Given a table of song_plays and a table of users, write a query to extract the earliest date each user played their third unique song. If a user has listened to less than three unique songs, display their name with a NULL date and song name.

10. How many more samples are needed to decrease the margin of error from 3 to 0.3?

Given a sample size n with a margin of error of 3, calculate the additional samples required to reduce the margin of error to 0.3.

11. What is the mean and variance of the distribution of 2X - Y?

Given X and Y are independent random variables with normal distributions, determine the mean and variance of 2X - Y.

12. How do you calculate the sample size and power for an AB test?

For an AB test with a test group and a control group: 1. How do you calculate the necessary sample size for accurate measurement? 2. If the sample size is sufficient, should the power increase or decrease to measure very small differences between the groups?

13. How does random forest generate the forest and why use it over logistic regression?

Explain how a random forest creates multiple decision trees and combines their results. Discuss the advantages of random forest over logistic regression, such as handling non-linear data and reducing overfitting.

14. How do we deal with missing square footage data to construct a housing price model?

You have 100K sold listings in Seattle, but 20% are missing square footage data. Describe methods to handle missing data, such as imputation, using algorithms that can handle missing values, or excluding incomplete records.

15. How do you detect and handle correlation between variables in linear regression?

Explain methods to identify correlated variables, such as correlation matrices or VIF. Describe techniques to handle correlation, like removing variables or using regularization. Discuss the impact of ignoring correlation on the regression model.

16. How would you design a model to detect potential bombs at a border crossing?

Describe the inputs and outputs for a bomb detection model at a border crossing. Explain how to measure accuracy and test the model, including data collection, feature engineering, and evaluation metrics.

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Samsung Electronics interview include:

1. Analyze Case Studies: Samsung’s questions might delve into your capability to analyze and derive insights from large datasets. Be prepared to tackle data-specific problems and present your solutions.

2. Understand Recommender Systems: Familiarity with machine learning algorithms, especially recommender systems, can be crucial. Review your work on similar projects and be ready to discuss them.

3. Stay Updated on Industry Trends: Samsung values innovation. Be aware of the latest trends in data science and how they can apply to marketing strategies or product optimization.

What is the average salary for a Data Scientist at Samsung Electronics?

According to Glassdoor, data scientists at Samsung Electronics earn between $114K to $160K per year, with an average of $135K per year.

What is the role of a Data Scientist at Samsung Electronics?

The role of a Data Scientist at Samsung Electronics involves designing and implementing machine learning solutions, analyzing vast datasets, developing predictive models, and providing actionable insights to support marketing and business channels. You will work with massive structured and unstructured data, deploy models in Big Data environments, and communicate findings clearly to various stakeholders.

What qualifications are required for the Data Scientist position at Samsung Electronics?

Candidates should have a Master’s or Ph.D. in Mathematics, Computer Science, Engineering, or Economics. They must have experience building and deploying recommender systems, be adept in machine learning solutions, and have 3-6 years of experience with Big Data environments like Hive and Spark. Strong analytical and problem-solving skills are essential in SQL, Python, Spark, or R.

What is the work culture like at Samsung Electronics?

Samsung’s dynamic and collaborative culture values innovation, inclusivity, and continuous learning. You will be part of a team that supports each other and works on pioneering products. The company also encourages a “Think and Jump right in” attitude, meaning you should be proactive, detail-oriented, and adaptive to new challenges.

How does Samsung support the personal and professional growth of its employees?

Samsung provides numerous benefits, such as medical, dental, and vision insurance, a 401(k), tuition assistance, paid time off, and student loan programs. Employees have access to resources like JIRA and Confluence for agile project planning and are encouraged to suggest new procedures and systems. Samsung’s inclusive culture and diverse workforce drive innovation and offer equal opportunities for all employees.

If you’re eager to work among the industry’s brightest minds and be part of a team that pushes the boundaries of Machine Learning and AI, this is the place to be. Samsung’s collaborative and inclusive culture ensures that every individual’s contributions are valued, making it an excellent environment for professional growth.

Planning to ace your interview? For more insights about the company, check out our main Samsung Interview Guide where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Samsung’s interview process for different positions.

You can also check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!