CGI Data Scientist Interview Questions + Guide in 2025

CGI is a global IT and business consulting services firm, committed to delivering high-quality solutions for their clients while fostering a culture of ownership and teamwork.

As a Data Scientist at CGI, you will play a critical role in analyzing complex datasets, developing machine learning models, and collaborating with cross-functional teams to drive data-driven decision-making. Key responsibilities include automating feature engineering, model training, and evaluation processes, as well as designing and maintaining ETL processes to ensure data quality and accessibility. You will also be expected to develop production-level dashboards and translate business requirements into actionable insights.

Required skills for this position include extensive experience in applied machine learning, proficiency in programming languages such as Python and SQL, and a strong understanding of data visualization and business intelligence tools. A successful Data Scientist at CGI will demonstrate strong analytical and problem-solving abilities, effective communication skills, and a proactive approach to learning new technologies. Familiarity with Natural Language Processing models and experience working in agile environments will further enhance your fit for this role.

This guide aims to equip you with the knowledge and confidence to excel in your interview by highlighting the specific skills and experiences that CGI values in a Data Scientist candidate.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at CGI. The interview process will likely focus on your technical expertise, problem-solving abilities, and experience with data analysis and machine learning. Be prepared to discuss your past projects, methodologies, and how you approach challenges in data science.

Experience and Background

1. Can you describe a project where you successfully implemented a machine learning model?

This question aims to assess your practical experience and understanding of machine learning processes.

How to Answer

Discuss the project scope, your role, the challenges faced, and the impact of the model. Highlight specific techniques used and any metrics that demonstrate success.

Example

“In my last role, I developed a predictive model for customer churn using logistic regression. I gathered data from various sources, performed feature engineering, and validated the model using cross-validation techniques. The model improved our retention rate by 15% over six months.”

Machine Learning

2. How do you approach feature engineering for a machine learning model?

This question evaluates your understanding of the importance of feature selection and transformation.

How to Answer

Explain your process for identifying relevant features, handling missing data, and transforming variables. Mention any tools or techniques you use.

Example

“I start by analyzing the dataset to understand the relationships between features and the target variable. I use techniques like one-hot encoding for categorical variables and normalization for numerical features. I also perform feature importance analysis to select the most impactful features.”

3. What methods do you use to evaluate the performance of a machine learning model?

This question tests your knowledge of model evaluation metrics.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. Explain how you choose the appropriate metric based on the problem context.

Example

“I typically use accuracy for balanced datasets, but for imbalanced classes, I prefer precision and recall. For instance, in a fraud detection model, I focus on recall to minimize false negatives, ensuring we catch as many fraudulent transactions as possible.”

4. Can you explain the concept of overfitting and how to prevent it?

This question assesses your understanding of model generalization.

How to Answer

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

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. To prevent it, I use techniques like k-fold cross-validation to ensure the model performs well on unseen data, and I apply L1 or L2 regularization to penalize overly complex models.”

Data Analysis

5. How do you ensure data quality before analysis?

This question evaluates your approach to data cleaning and preprocessing.

How to Answer

Discuss your methods for checking data integrity, handling missing values, and ensuring consistency.

Example

“I perform data profiling to identify anomalies and missing values. I use imputation techniques for missing data and standardize formats for consistency. Additionally, I validate data against known benchmarks to ensure accuracy.”

6. Describe a time when you had to analyze a complex dataset. What tools did you use?

This question assesses your analytical skills and familiarity with data analysis tools.

How to Answer

Provide details about the dataset, the tools you used (e.g., Python, R, SQL), and the insights you derived.

Example

“I worked on a project analyzing customer behavior data using Python and Pandas. I utilized SQL for data extraction and performed exploratory data analysis to identify trends. The insights led to targeted marketing strategies that increased engagement by 20%.”

Collaboration and Communication

7. How do you communicate complex data findings to non-technical stakeholders?

This question tests your ability to convey technical information clearly.

How to Answer

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

Example

“I focus on storytelling with data, using visualizations to highlight key insights. For instance, I created a dashboard using Tableau that presented our findings in an intuitive format, allowing stakeholders to grasp the implications quickly.”

8. Can you give an example of how you collaborated with a cross-functional team?

This question evaluates your teamwork and collaboration skills.

How to Answer

Describe a specific project, your role, and how you worked with others to achieve a common goal.

Example

“I collaborated with the marketing and IT teams to develop a customer segmentation model. I facilitated workshops to gather requirements and ensured that the model aligned with marketing strategies. This collaboration resulted in a successful campaign that increased sales by 30%.”

Problem-Solving

9. Describe a challenging data-related problem you faced and how you resolved it.

This question assesses your problem-solving skills and resilience.

How to Answer

Outline the problem, your analysis, the solution you implemented, and the outcome.

Example

“I encountered a significant data discrepancy in a sales report. I traced the issue back to an ETL process error. I re-engineered the data pipeline, implemented validation checks, and ensured data integrity moving forward. This not only resolved the issue but also improved our reporting accuracy.”

10. How do you stay updated with the latest trends in data science?

This question evaluates your commitment to continuous learning.

How to Answer

Mention specific resources, communities, or courses you engage with to stay informed.

Example

“I regularly read industry blogs, participate in online forums like Kaggle, and attend webinars. I also take courses on platforms like Coursera to learn about emerging technologies and methodologies in data science.”

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

Embrace a Conversational Approach

Interviews at CGI often have a conversational tone, especially in the initial rounds. Be prepared to discuss your past experiences in a narrative format. Highlight your projects and the impact they had, as this will resonate well with interviewers. They appreciate candidates who can articulate their journey and the lessons learned along the way.

Prepare for Behavioral Questions

Expect questions that delve into your past work experiences, particularly around challenges faced, mistakes made, and how you handled stress. Use the STAR (Situation, Task, Action, Result) method to structure your responses. This will help you provide clear and concise answers that demonstrate your problem-solving skills and resilience.

Showcase Your Technical Expertise

Given the technical nature of the Data Scientist role, be ready to discuss your proficiency in relevant programming languages and tools such as Python, SQL, and machine learning frameworks. Prepare to explain your experience with data cleaning, feature engineering, and model evaluation. You may also be asked to solve technical problems or case studies, so practice coding challenges and data analysis scenarios beforehand.

Understand the Company Culture

CGI values ownership, teamwork, and respect. Familiarize yourself with their mission and how your role as a Data Scientist contributes to it. Be prepared to discuss how you can embody these values in your work. Show enthusiasm for collaboration and innovation, as these are key components of their work environment.

Ask Insightful Questions

Interviews are a two-way street. Prepare thoughtful questions that demonstrate your interest in the role and the company. Inquire about the team dynamics, ongoing projects, and how success is measured in the Data Science department. This not only shows your engagement but also helps you assess if CGI is the right fit for you.

Be Ready for a Fast-Paced Environment

CGI operates in a dynamic setting, often requiring quick thinking and adaptability. Be prepared to discuss how you handle tight deadlines and shifting priorities. Share examples from your past experiences where you successfully navigated such challenges.

Follow Up Professionally

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the position and briefly mention a key point from your discussion that reinforces your fit for the role. This leaves a positive impression and keeps you top of mind for the interviewers.

By following these tips, you can present yourself as a well-rounded candidate who is not only technically proficient but also a great cultural fit for CGI. Good luck!

The interview process for a Data Scientist role at CGI is structured to assess both technical and interpersonal skills, ensuring candidates align with the company's values and project requirements. The process typically unfolds in several stages:

1. Initial Screening

The first step usually involves a phone interview with a recruiter or HR representative. This conversation is designed to gauge your interest in the role, discuss your resume, and evaluate your communication skills. Expect questions about your motivation for applying, your understanding of the company, and your salary expectations. This is also an opportunity for you to ask about the company culture and the specifics of the role.

2. Technical Assessment

Following the initial screening, candidates may be required to complete a technical assessment. This could involve take-home exercises or online tests that evaluate your data science skills, including programming in languages such as Python or R, SQL proficiency, and your ability to analyze datasets. The assessment may also include questions related to machine learning concepts, data visualization, and statistical analysis.

3. Managerial Interview

The next round typically involves an interview with a hiring manager or senior team member. This session is more conversational and focuses on your past experiences, specific projects you've worked on, and how you handle challenges in a work environment. Be prepared to discuss your technical skills in detail, including any relevant methodologies you've employed in previous roles. Questions may also touch on your ability to work under pressure and how you approach problem-solving.

4. Onsite Interview

For candidates who progress past the managerial interview, an onsite interview may be scheduled. This could involve multiple rounds with different team members, including technical and behavioral interviews. Expect to engage in discussions about your previous work, technical challenges you've faced, and how you collaborate with others. You may also be asked to solve real-time problems or case studies relevant to the role.

5. Final Interview

In some cases, a final interview may be conducted with higher-level management or stakeholders. This round often focuses on cultural fit and your long-term career aspirations. It’s a chance for you to demonstrate your alignment with CGI's values and mission, as well as your enthusiasm for contributing to the team.

Throughout the process, candidates are encouraged to ask questions about the team dynamics, project expectations, and growth opportunities within the company.

Now that you have an understanding of the interview process, let's delve into the specific questions that candidates have encountered during their interviews at CGI.

1. Write a function sum_to_n to find all combinations of integers that sum to a given value N.

Given a list of integers, and an integer N, write a function sum_to_n to find all combinations that sum to the value N.

2. What are the drawbacks of having student test scores organized in the given layouts?

Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in “messy” datasets.

3. How would you locate a mouse in a 4x4 grid using the fewest scans?

You have a 4x4 grid with a mouse trapped in one of the cells. You can scan subsets of cells to know if the mouse is within that subset. How would you determine the mouse’s location using the fewest number of scans?

4. How would you select Dashers for Doordash deliveries in NYC and Charlotte?

Doordash is launching delivery services in New York City and Charlotte and needs a process for selecting dashers. How would you decide which Dashers do these deliveries, and would the criteria for selection be the same for both cities?

5. What factors could bias Jetco’s study on-boarding times?

A study showed that Jetco, a new airline, has the fastest average boarding time. What factors could have biased this result, and what would you investigate?

6. How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company?

You work at a B2B SAAS company and are interested in testing different subscription pricing levels. Your project manager asks you to run a two-week-long A/B test to test an increase in pricing. How would you design this test and determine if the pricing increase is a good business decision?

7. How would you explain linear regression to a child, a first-year college student, and a seasoned mathematician?

Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician, tailoring each explanation to their understanding level.

8. What happens when you run logistic regression on perfectly linearly separable data?

Given a dataset of perfectly linearly separable data, describe the outcome when logistic regression is applied.

9. How would you evaluate and validate a decision tree model for predicting loan repayment?

As a data scientist at a bank, you need to build a decision tree model to predict loan repayment. Explain how you would evaluate if a decision tree is the right model and how you would assess its performance before and after deployment.

10. How would you justify using a neural network model to non-technical stakeholders?

If tasked with building a neural network model to solve a business problem, explain how you would justify the model’s complexity and explain its predictions to non-technical stakeholders.

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

Describe the process by which random forest generates its forest and explain why it might be preferred over other algorithms like logistic regression.

12. How much should we budget for the coupon initiative in total?

A ride-sharing app has a probability p of dispensing a $5 coupon to a rider. The app services N riders. Calculate the total budget needed for the coupon initiative.

13. What is a confidence interval for a statistic and why is it useful?

Explain what a confidence interval is, why it is useful to know the confidence interval for a statistic, and how to calculate it.

14. What is the probability that item X would be found on Amazon’s website?

Amazon has a warehouse system where items are located at different distribution centers. In one city, the probability that item X is available at warehouse A is 0.6 and at warehouse B is 0.8. Calculate the probability that item X would be found on Amazon’s website.

15. Is this a fair coin?

You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair.

16. What are time series models, and why do we need them?

Describe what time series models are and explain why they are needed when we have less complicated regression models.

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

1. Be Thorough About Your Experiences: Expect to discuss your previous work and projects in detail. Be ready to explain your thought processes and decision-making skills clearly.

2. Practice Behavioral Questions: Prepare to answer behavioral questions assessing how you handle stress, mistakes, and teamwork in team environments.

3. Understand the Technical Requirements: Be familiar with the technical skills listed in the job description, such as Python, SQL, ETL processes, and cloud platforms like AWS or Azure, as these may come up during the technical screening rounds.

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

What skills and qualifications does CGI seek in a Data Scientist candidate?

CGI seeks candidates with strong data mining, analysis, and machine learning skills. Experience with cloud platforms like AWS or Azure is preferred. Proficiency in programming languages such as Python and SQL is essential. Candidates should have a Bachelor’s degree in a relevant field; a Master’s degree is preferred. Familiarity with AI services and statistical analysis is also desirable.

What projects will Data Scientists work on at CGI?

Data Scientists at CGI will work on innovative projects involving emerging technologies. This includes designing state-of-the-art AI/ML solutions, implementing machine learning models, and collaborating with business teams to meet clients’ needs. Projects may encompass areas like speech, vision, and text AI services, leveraging cloud-native platforms for optimized performance.

What can candidates expect from CGI’s company culture?

CGI values ownership, teamwork, respect, and belonging. Employees, known as “members,” are empowered to participate fully in building a world-class company. The work environment encourages innovation, offers comprehensive benefits, and supports career growth and skills development. CGI fosters a collaborative and inclusive atmosphere where diverse expertise is valued.

Understanding CGI’s demands and preparing for various interview rounds can significantly enhance your hiring chances.

If you want more insights about the company, check out our main CGI Interview Guide, which covers 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 CGI’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!