Shopee Data Scientist Interview Questions + Guide in 2025

Shopee is a leading e-commerce platform in Southeast Asia and Taiwan, known for its commitment to providing a seamless online shopping experience.

As a Data Scientist at Shopee, you will be instrumental in analyzing vast datasets to uncover insights that drive business decisions. This role encompasses key responsibilities such as developing algorithms for predictive analytics, creating and optimizing SQL queries for data extraction, and employing machine learning techniques to enhance product offerings and customer experience. Ideal candidates will possess strong proficiency in Python, a solid understanding of algorithms, and experience with statistical analysis. A passion for problem-solving and an ability to communicate complex data findings in an accessible manner are essential traits for success in this fast-paced, data-driven environment.

This guide aims to equip you with the knowledge and confidence needed to excel in your interview, helping you to effectively showcase your technical skills and alignment with Shopee’s innovative culture.

The interview process for a Data Scientist role at Shopee is structured and consists of multiple stages designed to assess both technical skills and cultural fit.

1. Initial HR Screening

The process begins with an initial phone interview conducted by an HR representative. This conversation typically lasts around 30 minutes and focuses on your background, availability, and general fit for the company. The HR interviewer may also ask about your educational qualifications and previous work experiences to gauge your suitability for the role.

2. Online Assessment

Following the HR screening, candidates are required to complete an online assessment. This assessment usually consists of two coding questions that must be solved within a set time limit, often around 70 minutes. The questions are generally of varying difficulty, with one being easier and the other more challenging, often related to data structures and algorithms. Candidates are expected to have their cameras on during this assessment to ensure integrity.

3. Technical Interviews

After successfully completing the online assessment, candidates move on to the technical interview rounds. Typically, there are two to three rounds of technical interviews. The first technical interview focuses on coding skills, where candidates are asked to solve problems in real-time, often using a collaborative document. Questions may include topics such as SQL queries, Python programming, and algorithmic challenges, including dynamic programming and graph-related problems.

The subsequent technical interviews delve deeper into machine learning concepts and project experiences. Interviewers will ask candidates to discuss their past projects, the methodologies used, and the outcomes achieved. Behavioral questions may also be included to assess how candidates approach problem-solving and teamwork.

4. Final Evaluation

In some cases, there may be a final round of interviews where candidates meet with additional team members or senior staff. This round may include more in-depth discussions about technical expertise, industry knowledge, and how candidates can contribute to the team and company goals.

Throughout the process, candidates are encouraged to demonstrate their analytical thinking, problem-solving abilities, and familiarity with machine learning frameworks and statistical methods.

As you prepare for your interview, it's essential to be ready for the specific questions that may arise during these stages.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Shopee. The interview process will assess a combination of technical skills, problem-solving abilities, and your experience with data analysis and machine learning. Be prepared to demonstrate your knowledge of algorithms, SQL, Python, and machine learning concepts, as well as your ability to communicate your past project experiences effectively.

Algorithms

1. Can you explain the difference between depth-first search and breadth-first search?

Understanding these fundamental algorithms is crucial for any data scientist, especially when dealing with graph-related problems.

How to Answer

Discuss the basic principles of both algorithms, their use cases, and their time and space complexities.

Example

“Depth-first search explores as far as possible along each branch before backtracking, making it useful for scenarios like maze solving. In contrast, breadth-first search explores all neighbors at the present depth prior to moving on to nodes at the next depth level, which is ideal for finding the shortest path in unweighted graphs.”

2. How would you optimize a sorting algorithm?

This question tests your understanding of algorithm efficiency and optimization techniques.

How to Answer

Talk about different sorting algorithms and their complexities, and mention specific techniques to improve performance.

Example

“I would analyze the current sorting algorithm's time complexity and consider switching to a more efficient algorithm like quicksort or mergesort if the data set is large. Additionally, I would implement techniques like parallel processing to handle sorting in chunks, which can significantly reduce execution time.”

3. Describe a project where you implemented a machine learning algorithm. What challenges did you face?

This question assesses your practical experience with machine learning.

How to Answer

Focus on a specific project, the algorithm used, and the challenges encountered during implementation.

Example

“In a project predicting customer churn, I implemented a logistic regression model. One challenge was dealing with imbalanced data, which I addressed by using techniques like SMOTE to generate synthetic samples of the minority class, improving the model's accuracy.”

4. What is dynamic programming, and can you provide an example of where you used it?

Dynamic programming is a key concept in algorithm design, and this question tests your understanding of it.

How to Answer

Explain the concept of dynamic programming and provide a specific example from your experience.

Example

“Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. I used it in a project to optimize resource allocation, where I implemented the Knapsack problem to maximize profit while adhering to weight constraints.”

5. How do you handle missing data in a dataset?

Handling missing data is a common challenge in data science.

How to Answer

Discuss various strategies for dealing with missing data, including imputation and removal.

Example

“I typically assess the extent of missing data first. If it’s minimal, I might use mean or median imputation. For larger gaps, I consider removing those records or using predictive modeling to estimate the missing values based on other features.”

SQL

1. Write a SQL query to find the second highest salary from a table.

This question tests your SQL skills and understanding of database queries.

How to Answer

Explain your thought process and the SQL functions you would use.

Example

“I would use a subquery to first select the maximum salary from the table where the salary is less than the maximum salary. The query would look like this: SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees);”

2. How do you optimize SQL queries for performance?

This question assesses your knowledge of SQL optimization techniques.

How to Answer

Discuss indexing, query structure, and other optimization strategies.

Example

“I optimize SQL queries by ensuring proper indexing on frequently queried columns, avoiding SELECT *, and using JOINs judiciously. Additionally, I analyze query execution plans to identify bottlenecks and adjust the query accordingly.”

3. Can you explain the difference between INNER JOIN and LEFT JOIN?

Understanding joins is essential for data manipulation in SQL.

How to Answer

Clarify the differences in how these joins operate and their use cases.

Example

“An INNER JOIN returns only the rows that have matching values in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table, filling in NULLs for non-matching rows. This is useful when I want to retain all records from the left table regardless of matches.”

4. Describe a complex SQL query you wrote and the problem it solved.

This question allows you to showcase your SQL skills in a practical context.

How to Answer

Provide details about the query, the data involved, and the outcome.

Example

“I wrote a complex SQL query to analyze customer purchase patterns by joining multiple tables, including transactions, customers, and products. The query aggregated data to show the average purchase value per customer segment, which helped the marketing team tailor their campaigns effectively.”

5. How do you handle performance issues in SQL queries?

This question tests your problem-solving skills in a database context.

How to Answer

Discuss your approach to diagnosing and resolving performance issues.

Example

“I start by analyzing the query execution plan to identify slow operations. I then look for opportunities to add indexes, rewrite the query for efficiency, or partition large tables to improve performance. Regularly monitoring query performance helps catch issues early.”

Machine Learning

1. What machine learning algorithms are you most familiar with, and how have you applied them?

This question assesses your knowledge of machine learning algorithms.

How to Answer

Mention specific algorithms and provide examples of their application.

Example

“I am most familiar with algorithms like linear regression, decision trees, and random forests. In a recent project, I used a random forest classifier to predict customer churn, which provided a robust model with high accuracy due to its ability to handle non-linear relationships.”

2. How do you evaluate the performance of a machine learning model?

Understanding model evaluation is crucial for data scientists.

How to Answer

Discuss various metrics and techniques for model evaluation.

Example

“I evaluate model performance using metrics like accuracy, precision, recall, and F1-score, depending on the problem type. For classification tasks, I also use confusion matrices to visualize performance and ROC curves to assess the trade-off between true positive and false positive rates.”

3. Can you explain overfitting and how to prevent it?

This question tests your understanding of a common issue in machine learning.

How to Answer

Define overfitting and discuss strategies to mitigate it.

Example

“Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. To prevent it, I use techniques like cross-validation, regularization, and pruning decision trees, as well as ensuring I have a sufficiently large and diverse training dataset.”

4. Describe a time when you had to tune hyperparameters for a model. What approach did you take?

This question assesses your practical experience with model optimization.

How to Answer

Explain your approach to hyperparameter tuning and the results achieved.

Example

“In a project using a support vector machine, I employed grid search to tune hyperparameters like the kernel type and regularization parameter. By evaluating model performance on a validation set, I was able to identify the optimal parameters, which improved the model’s accuracy by 15%.”

5. How do you handle imbalanced datasets in machine learning?

This question tests your knowledge of techniques for dealing with imbalanced data.

How to Answer

Discuss various strategies for addressing class imbalance.

Example

“I handle imbalanced datasets by using techniques such as resampling, where I either oversample the minority class or undersample the majority class. Additionally, I may employ algorithms that are robust to class imbalance, like ensemble methods, or use cost-sensitive learning to penalize misclassifications of the minority class more heavily.”