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Indeed Data Scientist Interview Questions + Guide in 2025

Indeed Data Scientist Interview Questions + Guide in 2025

Overview

Indeed is the world's number one job site, helping people find jobs across various markets and languages with a strong commitment to innovation and inclusivity.

As a Data Scientist at Indeed, you will be responsible for building machine learning solutions that enhance the efficiency of software delivery. This role involves addressing significant challenges such as designing algorithms for faster regression testing and developing tools for automating root cause analysis. You will work closely with software engineers and other data scientists to improve Indeed's product offerings, ensuring they are effective in helping job seekers and employers alike. Key responsibilities include data extraction, cleansing, feature engineering, exploratory analysis, and experimental design. A successful candidate will have robust experience in machine learning, statistical analysis, and data visualization, along with strong programming skills in Python or Java. Additionally, familiarity with cloud services like AWS and experience in deploying deep learning models will be crucial.

At Indeed, we value individuals who are passionate about their work and strive for continuous improvement. This guide will provide you with insights and tailored questions to help you effectively prepare for your interview, ensuring you present your skills and experiences in alignment with Indeed's values and expectations.

Indeed.Com Data Scientist Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Indeed. The interview process will likely cover a range of topics including machine learning, statistics, coding, and data engineering. Candidates should be prepared to demonstrate their technical skills, problem-solving abilities, and understanding of data science principles.

Machine Learning

1. Explain how you would handle missing data in a dataset.

Handling missing data is crucial for maintaining the integrity of your analysis. Discuss various strategies such as imputation, deletion, or using algorithms that support missing values.

How to Answer

Explain the importance of understanding the nature of the missing data and the implications of each method. Provide examples of when you would use each approach.

Example

“I would first analyze the pattern of missing data to determine if it’s random or systematic. For random missing data, I might use mean or median imputation. However, if the missingness is systematic, I would consider using predictive modeling techniques to estimate the missing values or even explore the option of excluding those records if they are not significant.”

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 in real-world scenarios.

How to Answer

Outline the project scope, your role, the methodologies used, and the challenges faced. Emphasize your problem-solving approach and the outcomes.

Example

“In a project aimed at predicting customer churn, I faced challenges with feature selection due to high dimensionality. I implemented recursive feature elimination and used cross-validation to ensure the model's robustness. Ultimately, we achieved a 15% increase in prediction accuracy.”

3. What is the difference between supervised and unsupervised learning?

This question tests your foundational knowledge of machine learning concepts.

How to Answer

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

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as regression and classification tasks. In contrast, unsupervised learning deals with unlabeled data, focusing on finding hidden patterns, like clustering and association algorithms.”

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

Understanding model evaluation metrics is essential for data scientists.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and when to use each.

Example

“I evaluate model performance using multiple metrics. For classification tasks, I focus on precision and recall to understand the trade-off between false positives and false negatives. For regression tasks, I use RMSE and R-squared to assess the model's predictive power.”

Statistics & Probability

1. Explain Bayes' theorem and its application in data science.

This question assesses your understanding of probability theory and its relevance to data science.

How to Answer

Define Bayes' theorem and provide a practical example of its application.

Example

“Bayes' theorem describes the probability of an event based on prior knowledge of conditions related to the event. In data science, it’s often used in spam detection algorithms, where the model updates the probability of an email being spam based on the presence of certain keywords.”

2. What is A/B testing, and how would you design an experiment?

A/B testing is a common method for evaluating changes in products or features.

How to Answer

Outline the steps for designing an A/B test, including hypothesis formulation, sample size determination, and analysis of results.

Example

“I would start by defining a clear hypothesis, such as ‘Changing the button color will increase click-through rates.’ Next, I would determine the sample size needed for statistical significance, randomly assign users to control and treatment groups, and analyze the results using a t-test to compare conversion rates.”

3. Can you explain the concept of p-value?

Understanding p-values is crucial for hypothesis testing.

How to Answer

Define p-value and its significance in hypothesis testing.

Example

“The p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that the observed effect is statistically significant.”

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

This question tests your knowledge of statistical principles.

How to Answer

Explain the Central Limit Theorem and its implications for sampling distributions.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is important because it allows us to make inferences about population parameters using sample statistics.”

Coding & Data Engineering

1. Write a function to merge two sorted lists.

This question assesses your coding skills and understanding of data structures.

How to Answer

Discuss your approach to merging the lists efficiently, considering time and space complexity.

Example

“I would use a two-pointer technique to iterate through both lists, comparing elements and appending the smaller one to the result list. This approach runs in O(n + m) time complexity, where n and m are the lengths of the two lists.”

2. How would you optimize a SQL query?

This question evaluates your SQL skills and understanding of database performance.

How to Answer

Discuss techniques such as indexing, query restructuring, and analyzing execution plans.

Example

“To optimize a SQL query, I would first analyze the execution plan to identify bottlenecks. I might add indexes on frequently queried columns, avoid SELECT *, and restructure the query to minimize joins and subqueries where possible.”

3. Explain the difference between a primary key and a foreign key.

This question tests your understanding of database design.

How to Answer

Define both terms and explain their roles in relational databases.

Example

“A primary key uniquely identifies each record in a table, ensuring that no two rows have the same value. A foreign key, on the other hand, is a field in one table that links to the primary key of another table, establishing a relationship between the two tables.”

4. Describe your experience with data extraction and cleansing.

This question assesses your practical experience with data preprocessing.

How to Answer

Discuss the tools and techniques you have used for data extraction and cleansing.

Example

“I have experience using Python libraries like Pandas for data extraction and cleansing. I typically use functions to handle missing values, remove duplicates, and standardize formats, ensuring the dataset is clean and ready for analysis.”

5. How do you ensure data quality in your projects?

This question evaluates your approach to maintaining data integrity.

How to Answer

Discuss methods for validating and verifying data quality throughout the data lifecycle.

Example

“I ensure data quality by implementing validation checks at the data entry stage, conducting regular audits, and using automated scripts to identify anomalies. Additionally, I establish clear data governance policies to maintain standards across the organization.”

Question
Topics
Difficulty
Ask Chance
Machine Learning
Hard
Very High
Python
R
Algorithms
Easy
Very High
Machine Learning
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Medium
Very High
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SQL
Easy
Medium
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SQL
Medium
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SQL
Hard
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Analytics
Medium
Medium
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Machine Learning
Easy
Very High
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Machine Learning
Medium
Very High
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SQL
Easy
Medium
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SQL
Hard
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SQL
Easy
Low
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Machine Learning
Medium
Very High
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SQL
Medium
High
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Analytics
Easy
Very High
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Machine Learning
Hard
Medium
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Analytics
Easy
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SQL
Easy
Medium
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Machine Learning
Medium
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Hard
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View all Indeed.Com Data Scientist questions

Indeed.Com Data Scientist Interview Tips

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

Understand the Interview Structure

Indeed has a structured interview process that typically includes a take-home assignment, technical interviews, and possibly a final behavioral interview. Familiarize yourself with each stage and prepare accordingly. For the take-home assignment, ensure you allocate enough time to complete it thoroughly, as it can take longer than the suggested time. Be ready to discuss your approach and findings in detail during the subsequent interviews.

Prepare for Technical Questions

Expect a mix of coding, statistics, and machine learning questions. Brush up on your knowledge of algorithms, data structures, and statistical concepts. Practice coding problems on platforms like LeetCode, focusing on easy to medium-level questions, as well as more complex problems related to data manipulation and analysis. Be prepared to explain your thought process clearly and concisely, as interviewers will be interested in how you approach problem-solving.

Showcase Your Full-Stack Data Science Skills

Indeed values candidates who have a comprehensive understanding of the data science lifecycle, from data extraction and cleansing to model deployment and monitoring. Be ready to discuss your experience with various tools and technologies, such as Python, SQL, and AWS services like SageMaker. Highlight any projects where you have successfully implemented machine learning models or data-driven solutions.

Communicate Effectively

Strong communication skills are essential, especially when discussing technical concepts with non-technical stakeholders. Practice explaining complex ideas in simple terms, and be prepared to articulate how your work impacts the business. During the interview, engage with your interviewers by asking clarifying questions and demonstrating your enthusiasm for the role and the company.

Be Ready for Behavioral Questions

Indeed places importance on cultural fit and teamwork. Prepare for behavioral questions that assess your collaboration skills, problem-solving abilities, and adaptability. Use the STAR (Situation, Task, Action, Result) method to structure your responses, providing specific examples from your past experiences that demonstrate your capabilities and alignment with Indeed's values.

Stay Informed About the Company

Research Indeed's products, mission, and recent developments in the job market. Understanding the company's goals and challenges will allow you to tailor your responses and show how your skills can contribute to their success. Be prepared to discuss how your background aligns with Indeed's mission to help people get jobs and improve the job search experience.

Manage Your Time and Expectations

The interview process can be lengthy and may involve multiple rounds. Be patient and proactive in following up with recruiters if you experience delays. Set realistic expectations for the timeline and be prepared for potential changes in the interview schedule.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Data Scientist role at Indeed. Good luck!

Indeed.Com Data Scientist Interview Process

The interview process for a Data Scientist role at Indeed is structured and involves multiple stages designed to assess both technical and interpersonal skills. Here’s a breakdown of the typical process:

1. Initial Recruiter Call

The process begins with a phone call from a recruiter. This conversation typically lasts about 30 minutes and serves as an introduction to the role and the company. The recruiter will discuss your background, experience, and motivations for applying, while also providing insights into the company culture and expectations for the position.

2. Take-Home Assignment

Following the initial call, candidates are usually given a take-home assignment. This task often involves analyzing a dataset and building a predictive model, such as salary prediction or other relevant data science challenges. Candidates are typically given a week to complete this assignment, and it is crucial to demonstrate a clear understanding of data science principles, as well as the ability to communicate your findings effectively.

3. Technical Interview

Once the take-home assignment is submitted, candidates may be invited to a technical interview, which is usually conducted via video call. This interview typically lasts about an hour and involves two current Data Scientists. Expect questions that cover a range of topics, including coding challenges (often on platforms like HackerRank), statistics, probability, and machine learning concepts. Candidates should be prepared to solve problems live and explain their thought processes.

4. Onsite Interviews

The final stage of the interview process is an onsite interview, which can be quite intensive. This usually consists of multiple rounds (often four or more) of interviews, each lasting about an hour. The rounds may include: - Math/Statistics Interview: Questions may cover hypothesis testing, regression analysis, and other statistical methods. - Machine Learning Interview: Candidates may be asked to discuss machine learning algorithms, model evaluation, and practical applications relevant to Indeed's products. - Coding Interview: This round often involves whiteboard coding exercises where candidates solve algorithmic problems or data manipulation tasks. - Behavioral Interview: This final round focuses on assessing cultural fit and communication skills, where candidates may be asked about past experiences and how they handle various work situations.

5. Final Discussion

After the technical rounds, there may be a final discussion with a senior team member or manager. This is an opportunity for candidates to ask questions about the team, projects, and company culture, while also allowing the interviewers to gauge the candidate's interest and fit for the role.

As you prepare for your interview, be ready to tackle a variety of technical challenges and demonstrate your problem-solving skills, as well as your ability to communicate complex ideas clearly. Next, let’s delve into the specific interview questions that candidates have encountered during the process.

What Indeed.Com Looks for in a Data Scientist

A/B TestingAlgorithmsAnalyticsMachine LearningProbabilityProduct MetricsPythonSQLStatistics
Indeed.Com Data Scientist
Average Data Scientist

1. How can PG&E model the electricity supply needed for a town?

PG&E needs to forecast the exact amount of electricity to supply a town each year. Supplying too little causes outages, while supplying too much wastes money. What is one way to model the required electricity supply?

How to Prepare for a Data Scientist Interview at Indeed

To help you succeed in your Indeed data scientist interviews, consider these tips based on interview experiences:

  1. Brush Up on Mathematical and Analytical Skills: Indeed places a strong emphasis on analytic and mathematical capabilities. Be sure to review concepts related to probabilities, statistics, and basic math problems.
  2. Be Prepared for Technical Interviews: Indeed uses platforms like Karat for technical screenings, which involve solving coding problems and discussing your thought process. Practice extensively with tools such as Interview Query to bolster your readiness.
  3. Know Your Projects Well: During resume deep dives and manager interviews, your previous projects will be scrutinized. Be ready to discuss the technical details, challenges you faced, and the impact your work had.

FAQs

What is the average salary for a Data Scientist at Indeed?

$134,304

Average Base Salary

$155,169

Average Total Compensation

Min: $87K
Max: $198K
Base Salary
Median: $130K
Mean (Average): $134K
Data points: 61
Min: $26K
Max: $270K
Total Compensation
Median: $143K
Mean (Average): $155K
Data points: 19

View the full Data Scientist at Indeed.Com salary guide

What technical skills are required for a Data Scientist role at Indeed?

You should have strong skills in data extraction, cleansing, feature engineering, and machine learning. Proficiency in Python or Java is essential, along with experience in SQL. Familiarity with tools like Spark, Presto, AWS (Athena, SageMaker), and data visualization techniques is highly beneficial.

What kind of projects will I work on as a Data Scientist at Indeed?

As a Data Scientist, you will work on building machine-learning solutions to improve software delivery efficiency. This includes designing algorithms for quicker regression testing, automating root cause analysis, and enhancing overall code quality. You’ll engage in data extraction, analysis, and modeling to make impactful business decisions.

What is the company culture like at Indeed?

Indeed is known for its inclusive and innovative culture. The company emphasizes improving people’s lives through better job search experiences. You’ll work with diverse teams across global engineering hubs, fostering collaboration and continuous learning.

Never Get Stuck with an Interview Question Again

Conclusion

If you want more insights about the company, check out our main Indeed 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 Indeed’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Indeed data scientist interview question and challenge.

You can 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!