Wish Data Scientist Interview Questions + Guide in 2025

Wish is a disruptive mobile e-commerce platform that connects millions of customers with a broad selection of affordable products delivered directly to their doors.

As a Data Scientist at Wish, you will be at the forefront of utilizing data to drive business decisions in a fast-paced environment. Your primary responsibilities will include conducting in-depth analyses using both internal and external data, helping to identify growth drivers and optimization opportunities across various business sectors such as merchant growth, logistics, and customer service. You will work closely with cross-functional teams to gather requirements and present actionable insights, all while maintaining the integrity of reporting and dashboards.

This role requires a solid foundation in computer science and mathematics, with significant experience in data analysis and experimentation. You will need to be proficient in SQL and have working knowledge of Python or similar programming languages. Strong analytical skills, problem-solving abilities, and excellent communication are crucial traits for success in this position, as you will be expected to translate complex data findings into clear insights for stakeholders.

At Wish, being self-motivated and independent while maintaining a collaborative spirit aligns well with the company's values of innovation and curiosity. This guide will equip you with the necessary knowledge and insight to navigate the interview process successfully, positioning you to demonstrate your fit for this exciting role.

The interview process for a Data Scientist role at Wish is structured to assess both technical and interpersonal skills, ensuring candidates are well-rounded and fit for the dynamic environment of the company. The process typically unfolds in several key stages:

1. Initial HR Screening

The first step involves a phone interview with an HR representative. This conversation usually lasts about 30-60 minutes and focuses on your background, motivations for applying, and understanding of Wish's culture. The HR representative will also discuss the role's expectations and gauge your fit within the team.

2. Technical Assessment

Following the HR screening, candidates are often required to complete a technical assessment. This may take place on platforms like HackerRank or CodeSignal and typically includes SQL queries and Python coding challenges. Expect to encounter medium-level SQL questions that test your ability to manipulate and analyze data, as well as basic coding problems that assess your programming skills.

3. Technical Phone Interviews

Candidates who perform well in the technical assessment will move on to one or two technical phone interviews. These interviews are conducted by data scientists and focus on your problem-solving abilities, knowledge of algorithms, and understanding of statistical concepts. You may be asked to solve coding problems in real-time, discuss your previous projects, and demonstrate your analytical thinking.

4. Onsite Interviews

The final stage consists of an onsite interview, which is typically divided into multiple rounds. Candidates can expect around four one-on-one interviews, each lasting about an hour. These rounds will cover a mix of technical and product-related questions, including experimentation design, data infrastructure challenges, and case studies relevant to Wish's business. Interviewers may also assess your ability to communicate complex technical concepts to non-technical stakeholders.

5. Final Review and Feedback

After the onsite interviews, candidates will receive feedback from the interviewers, usually communicated through a follow-up call with HR. This is also the stage where any final questions about the role or company can be addressed.

As you prepare for your interview, it's essential to be ready for a variety of questions that will test your technical skills, problem-solving abilities, and understanding of data science principles.

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

Understand the Interview Structure

The interview process at Wish typically includes an HR screening, followed by technical assessments that may involve coding challenges and product-related case studies. Familiarize yourself with this structure so you can prepare accordingly. Expect multiple rounds, including coding interviews focused on SQL and Python, as well as discussions on experimentation and product metrics. Knowing the flow will help you manage your time and energy effectively during the interview.

Master SQL and Python

Given the emphasis on SQL (33.81%) and Python (13.67%) in the role, ensure you are well-versed in writing complex SQL queries, including joins, group by, and window functions. Practice coding problems on platforms like LeetCode, focusing on medium to hard difficulty levels. For Python, brush up on data manipulation libraries such as Pandas and NumPy, and be prepared to demonstrate your coding skills in a live environment.

Prepare for Product and Experimentation Questions

Expect to face questions that assess your understanding of product metrics and experimentation. Be ready to discuss how you would design experiments to test hypotheses, analyze results, and derive actionable insights. Familiarize yourself with common metrics used in e-commerce and be prepared to explain how you would evaluate the success of a product feature or marketing campaign.

Showcase Your Analytical Skills

Wish values strong analytical and problem-solving skills. Be prepared to discuss past projects where you transformed data into business insights. Use the STAR (Situation, Task, Action, Result) method to structure your responses, highlighting your thought process and the impact of your work. This will demonstrate your ability to think critically and apply your skills to real-world problems.

Communicate Clearly and Effectively

As a Data Scientist at Wish, you will need to communicate complex data insights to non-technical stakeholders. Practice explaining technical concepts in simple terms and be prepared to discuss how you would present your findings through dashboards or visualizations. Clear communication is key, so consider practicing with peers or mentors to refine your delivery.

Embrace the Company Culture

Wish promotes a supportive environment that encourages curiosity and innovation. Show your enthusiasm for the company’s mission and values during the interview. Be prepared to discuss how your personal values align with Wish’s commitment to diversity and inclusion. This will help you connect with your interviewers and demonstrate that you are a good cultural fit.

Follow Up and Stay Engaged

After your interview, consider sending a thank-you email to express your appreciation for the opportunity and reiterate your interest in the role. This not only shows professionalism but also keeps you on the interviewers' radar. If you have any questions or need clarification on the next steps, don’t hesitate to reach out to your recruiter.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Scientist role at Wish. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Wish. The interview process will assess your technical skills in SQL, Python, and algorithms, as well as your ability to analyze data and derive actionable insights. Be prepared to discuss your past projects, problem-solving approaches, and how you can contribute to the company's mission of leveraging data for business growth.

SQL and Data Manipulation

1. Can you explain the difference between INNER JOIN and LEFT JOIN in SQL?

Understanding SQL joins is crucial for data manipulation and analysis.

How to Answer

Clearly define both types of joins and provide examples of when you would use each. Highlight the importance of understanding data relationships.

Example

"An INNER JOIN returns only the rows where there is a match in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. For instance, if I want to list all customers and their orders, I would use a LEFT JOIN to ensure I include customers who haven't placed any orders."

2. How would you write a SQL query to find the top 10 products by sales?

This question tests your ability to write efficient SQL queries.

How to Answer

Outline the steps you would take to aggregate sales data and sort it to find the top products.

Example

"I would use a SELECT statement to aggregate sales data, applying a SUM function on the sales column, and then use ORDER BY to sort the results in descending order, limiting the output to 10 rows."

3. What are window functions in SQL, and can you provide an example of their use?

Window functions are essential for advanced data analysis.

How to Answer

Explain what window functions are and how they differ from regular aggregate functions, providing a specific use case.

Example

"Window functions allow you to perform calculations across a set of table rows related to the current row. For example, I could use the ROW_NUMBER() function to assign a unique rank to each product based on sales within its category."

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

This question assesses your practical experience with SQL.

How to Answer

Discuss the context of the problem, the complexity of the query, and the outcome.

Example

"I once wrote a complex query that involved multiple joins and subqueries to analyze customer purchasing patterns over time. This helped the marketing team identify seasonal trends and adjust their strategies accordingly."

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

Handling missing data is a critical skill for data analysis.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation or removal, and the rationale behind your choice.

Example

"I typically assess the extent of missing data first. If it's minimal, I might remove those records. For larger gaps, I would consider imputation methods, such as using the mean or median for numerical data, to maintain the dataset's integrity."

Machine Learning and Statistics

1. Explain the difference between supervised and unsupervised learning.

This question tests your foundational knowledge of machine learning.

How to Answer

Define both terms and provide examples of algorithms used in each category.

Example

"Supervised learning involves training a model on labeled data, such as using regression or classification algorithms. In contrast, unsupervised learning deals with unlabeled data, where clustering algorithms like K-means are used to find patterns."

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

Understanding model evaluation is key to data science.

How to Answer

Discuss various metrics used for evaluation, depending on the type of problem (classification vs. regression).

Example

"I evaluate classification models using metrics like accuracy, precision, recall, and F1-score, while for regression models, I look at R-squared, mean absolute error, and root mean square error to assess performance."

3. Can you describe a machine learning project you worked on and the impact it had?

This question allows you to showcase your practical experience.

How to Answer

Detail the project, your role, the techniques used, and the results achieved.

Example

"I worked on a project to predict customer churn using logistic regression. By analyzing customer behavior data, we identified key factors leading to churn and implemented targeted retention strategies, resulting in a 15% decrease in churn rates."

4. What is overfitting, and how can it be prevented?

Understanding overfitting is crucial for building robust models.

How to Answer

Define overfitting and discuss techniques to prevent it, such as cross-validation and regularization.

Example

"Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern. To prevent it, I use techniques like cross-validation to ensure the model generalizes well and apply regularization methods to penalize overly complex models."

5. How do you approach feature selection for a machine learning model?

Feature selection is vital for model performance.

How to Answer

Discuss methods for selecting relevant features, such as correlation analysis or using algorithms like LASSO.

Example

"I start with exploratory data analysis to identify correlations and then use techniques like recursive feature elimination or LASSO regression to select the most impactful features, ensuring the model remains interpretable and efficient."

Data Analysis and Business Insights

1. How do you define and validate metrics for business performance?

This question assesses your understanding of metrics in a business context.

How to Answer

Discuss the importance of aligning metrics with business goals and the validation process.

Example

"I define metrics based on key business objectives, ensuring they are measurable and actionable. Validation involves testing the metrics against historical data to confirm their reliability and relevance to decision-making."

2. Describe a time when your analysis led to a significant business decision.

This question allows you to demonstrate your impact on the organization.

How to Answer

Share a specific example, detailing the analysis performed and the resulting decision.

Example

"During a sales analysis, I discovered that a particular product line was underperforming due to pricing issues. My analysis led to a pricing strategy adjustment, which increased sales by 20% within three months."

3. How do you communicate complex data insights to non-technical stakeholders?

Effective communication is key in data science roles.

How to Answer

Discuss your approach to simplifying complex concepts and using visual aids.

Example

"I focus on storytelling with data, using clear visuals and analogies to explain complex insights. For instance, I created a dashboard that highlighted key trends in sales data, making it easy for stakeholders to grasp the implications quickly."

4. What tools do you use for data visualization, and why?

This question assesses your familiarity with data visualization tools.

How to Answer

Mention specific tools and their advantages in presenting data.

Example

"I primarily use Tableau for its user-friendly interface and powerful visualization capabilities. It allows me to create interactive dashboards that help stakeholders explore data insights dynamically."

5. How do you prioritize multiple data projects with competing deadlines?

This question evaluates your project management skills.

How to Answer

Discuss your approach to prioritization and time management.

Example

"I prioritize projects based on their impact on business goals and deadlines. I use project management tools to track progress and communicate with stakeholders to ensure alignment on priorities and expectations."