KPMG Data Scientist Interview Questions + Guide in 2025

KPMG is a global leader in audit, tax, and advisory services, leveraging emerging technologies to address complex business challenges.

As a Data Scientist at KPMG, you will be integral to transforming raw data into actionable insights that drive strategic decision-making for clients across various industries, including technology, finance, government, and utilities. Your key responsibilities will include conducting data analysis and preprocessing, developing and implementing machine learning algorithms, and collaborating with cross-functional teams to address specific business problems through innovative AI solutions. Proficiency in programming languages like Python or R, along with a strong grasp of statistical concepts and methodologies such as regression and clustering, is essential. You will also be expected to communicate complex technical details to non-technical stakeholders, demonstrating your ability to bridge the gap between data science and business objectives.

This guide is designed to equip you with a deep understanding of the role and its alignment with KPMG's consulting practices, thereby enhancing your preparation for the interview process.

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

1. Initial Screening

The first step usually involves a brief phone call with a recruiter or HR representative. This initial screening lasts around 10 to 30 minutes and focuses on your background, experience, and salary expectations. The recruiter will also provide insights into the company culture and the specifics of the role, ensuring that you understand what KPMG is looking for in a candidate.

2. Technical Phone Interview

Following the initial screening, candidates typically undergo a technical phone interview. This round is conducted by a data scientist or a technical manager and lasts approximately 30 to 45 minutes. During this interview, you will be asked to discuss your previous projects, technical skills, and relevant experience. Expect questions related to data analysis, machine learning algorithms, and statistical methods. You may also be required to solve problems or answer technical questions on the spot.

3. Onsite Interview

The onsite interview is a more comprehensive evaluation and usually consists of multiple rounds. Candidates can expect to meet with several team members, including data scientists, managers, and possibly directors. This stage typically includes a mix of technical assessments, case studies, and behavioral interviews. You may be asked to present a past project or conduct a live coding exercise. The technical interviews will delve deeper into your understanding of machine learning, data preprocessing, and model development, while the behavioral interviews will assess your problem-solving skills and cultural fit within the team.

4. Final Interview

In some cases, a final interview may be conducted with higher management or a partner. This round often focuses on your motivation for joining KPMG, your long-term career goals, and how you can contribute to the firm. It may also include discussions about your salary expectations and willingness to travel, as the role may require significant travel.

Throughout the interview process, candidates should be prepared to demonstrate their technical expertise, problem-solving abilities, and communication skills, as these are critical for success in the Data Scientist role at KPMG.

Next, let’s explore the specific interview questions that candidates have encountered during this process.

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

Understand the Technical Landscape

Given KPMG's focus on advanced analytics and emerging technologies, it's crucial to familiarize yourself with the latest trends in AI, machine learning, and data science. Brush up on your knowledge of statistical methods, data modeling, and machine learning algorithms. Be prepared to discuss how you have applied these concepts in real-world scenarios, as interviewers will likely ask for specific examples from your past work.

Prepare for Exploratory Data Analysis (EDA)

Many candidates have noted that KPMG interviews often include questions about exploratory data analysis. Make sure you can articulate the steps involved in EDA, including data cleaning, validation, and the techniques you use to identify patterns and insights. Be ready to discuss how you would approach a dataset, what tools you would use, and how you would communicate your findings to stakeholders.

Communicate Clearly and Concisely

KPMG values the ability to explain complex technical concepts to non-technical stakeholders. Practice articulating your thoughts clearly and concisely. Use the STAR (Situation, Task, Action, Result) method to structure your responses, especially when discussing past projects. This will help you convey your experience effectively and demonstrate your communication skills.

Expect a Mix of Behavioral and Technical Questions

Interviews at KPMG often include both behavioral and technical components. Be prepared to discuss your previous experiences, how you handle challenges, and your approach to teamwork. For the technical portion, review common data science concepts, including regression analysis, clustering, and machine learning techniques. You may also be asked to solve problems on the spot, so practice coding challenges and be ready to explain your thought process.

Be Ready for Multiple Rounds

Candidates have reported that the interview process at KPMG can involve several rounds, including phone screenings and in-person interviews. Stay organized and keep track of your interview schedule. Prepare for each round by reviewing the job description and aligning your skills and experiences with the requirements outlined.

Showcase Your Collaborative Skills

KPMG emphasizes teamwork and collaboration. Be prepared to discuss how you have worked with cross-functional teams in the past. Highlight your ability to collaborate with software engineers, product managers, and other stakeholders to develop innovative solutions. Share examples of how you have contributed to team success and navigated challenges in a collaborative environment.

Stay Professional and Patient

Some candidates have expressed frustration with the interview process, citing delays and lack of communication. Regardless of your experience, maintain professionalism throughout the process. If you encounter any issues, such as scheduling conflicts or unclear communication, address them calmly and respectfully. This will reflect positively on your character and professionalism.

Follow Up Thoughtfully

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity to interview. Use this as a chance to reiterate your interest in the role and briefly mention any key points from the interview that you found particularly engaging. This can help keep you top of mind as they make their decision.

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

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at KPMG. The interview process will likely assess your technical skills in data analysis, machine learning, and statistical modeling, as well as your ability to communicate complex concepts to non-technical stakeholders. Be prepared to discuss your past experiences, problem-solving approaches, and how you can contribute to KPMG's innovative projects.

Data Analysis and Preprocessing

1. Describe the steps you take in exploratory data analysis (EDA).

Understanding EDA is crucial for any data scientist, as it helps in uncovering patterns and insights from data.

How to Answer

Outline the key steps you follow, such as data cleaning, visualization, and statistical analysis. Emphasize the importance of understanding the data before modeling.

Example

“In my EDA process, I start with data cleaning to handle missing values and outliers. Then, I visualize the data using histograms and scatter plots to identify trends and relationships. Finally, I perform statistical tests to validate my findings and ensure the data is ready for modeling.”

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

Handling missing data is a common challenge in data science, and your approach can significantly impact model performance.

How to Answer

Discuss various techniques such as imputation, deletion, or using algorithms that support missing values. Tailor your response to the context of the project.

Example

“I typically handle missing data by first assessing the extent of the missingness. If it's minimal, I might use mean or median imputation. For larger gaps, I consider using predictive models to estimate missing values or even dropping those records if they are not critical to the analysis.”

3. What are the preprocessing steps you follow before building a machine learning model?

Preprocessing is vital for ensuring the quality of your model's input data.

How to Answer

Mention steps like normalization, encoding categorical variables, and splitting the dataset into training and testing sets.

Example

“Before building a model, I normalize numerical features to ensure they are on a similar scale. I also encode categorical variables using one-hot encoding and split the dataset into training and testing sets to evaluate model performance accurately.”

4. Can you explain the importance of feature selection?

Feature selection can greatly influence the performance of your model.

How to Answer

Discuss how it helps in reducing overfitting, improving model accuracy, and decreasing computational cost.

Example

“Feature selection is crucial as it helps in reducing overfitting by eliminating irrelevant features. This not only improves model accuracy but also speeds up the training process, making it more efficient.”

5. How do you assess the quality of your data?

Data quality is essential for reliable analysis and modeling.

How to Answer

Talk about methods like data profiling, validation checks, and consistency checks.

Example

“I assess data quality through data profiling, which includes checking for duplicates, inconsistencies, and outliers. I also perform validation checks to ensure that the data meets the expected formats and ranges.”

Machine Learning

1. Explain the difference between supervised and unsupervised learning.

Understanding these concepts is fundamental to machine learning.

How to Answer

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

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 algorithms like clustering are used to find hidden patterns.”

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

Model evaluation is critical for understanding its effectiveness.

How to Answer

Discuss metrics like accuracy, precision, recall, F1 score, and ROC-AUC, depending on the problem type.

Example

“I evaluate model performance using metrics such as accuracy for classification tasks and mean squared error for regression. Additionally, I look at precision and recall to understand the trade-offs between false positives and false negatives.”

3. Describe a machine learning project you worked on and the challenges you faced.

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

How to Answer

Provide a brief overview of the project, the challenges encountered, and how you overcame them.

Example

“In a recent project, I developed a predictive model for customer churn. One challenge was dealing with imbalanced classes. I addressed this by using techniques like SMOTE for oversampling the minority class and adjusting the classification threshold.”

4. What algorithms do you prefer for classification tasks and why?

Your choice of algorithms can reflect your understanding of their strengths and weaknesses.

How to Answer

Mention a few algorithms and discuss their suitability for different scenarios.

Example

“I often use Random Forest for classification tasks due to its robustness against overfitting and ability to handle large datasets. For simpler problems, I might opt for logistic regression for its interpretability.”

5. How do you deal with overfitting in your models?

Overfitting is a common issue in machine learning, and your approach to it is crucial.

How to Answer

Discuss techniques like cross-validation, regularization, and pruning.

Example

“To combat overfitting, I use cross-validation to ensure my model generalizes well to unseen data. I also apply regularization techniques like L1 and L2 to penalize overly complex models.”

Statistical Knowledge

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

Understanding statistical significance is key in data analysis.

How to Answer

Define p-value and its role in hypothesis testing.

Example

“The p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating statistical significance.”

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

This theorem is fundamental in statistics and data science.

How to Answer

Explain the 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.”

3. How do you interpret a confidence interval?

Confidence intervals provide insight into the reliability of estimates.

How to Answer

Discuss what a confidence interval represents and how to interpret it.

Example

“A confidence interval gives a range of values within which we expect the true population parameter to lie, with a certain level of confidence, typically 95%. For instance, if a 95% confidence interval for a mean is [10, 15], we can say we are 95% confident that the true mean lies within this range.”

4. What is the difference between Type I and Type II errors?

Understanding these errors is crucial for hypothesis testing.

How to Answer

Define both types of errors and their implications.

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. Balancing these errors is essential in hypothesis testing to minimize incorrect conclusions.”

5. Explain the concept of regression analysis.

Regression analysis is a fundamental statistical technique.

How to Answer

Discuss its purpose and the types of regression.

Example

“Regression analysis is used to understand the relationship between dependent and independent variables. It can be linear, where we model a straight-line relationship, or non-linear, depending on the data's nature.”