CGI Data Scientist Interview Questions + Guide in 2024

CGI is a global leader in IT and business consulting services, known for its comprehensive solutions in data analytics, cybersecurity, and digital transformation. With a strong presence across industries, CGI serves private and public sector clients, leveraging technology to solve complex business challenges. As of 2024, CGI employs over 90,000 professionals worldwide, driving innovation through a client-first approach.

Joining CGI as a Data Scientist allows you to apply your data modeling, machine learning, and data-driven decision-making expertise to real-world problems. As a Data Scientist at CGI, you’ll engage with various data projects, from optimizing business processes to implementing predictive analytics and AI solutions.

If you’re preparing for this role, this guide is for you. We’ll break down the interview process, highlight key CGI data scientist interview questions, and offer tips to help you navigate the challenges ahead. Let’s dive in!

The interview process usually depends on the role and seniority; however, you can expect the following on a CGI data scientist interview:

Recruiter/Hiring Manager Call Screening

Once your application is shortlisted, you may be contacted by a recruiter from the CGI Talent Acquisition Team. This initial call typically involves verifying key details like your experiences, technical skill level, and application motivation. The recruiter may also ask basic behavioral questions to assess your suitability for the CGI culture.

In some instances, a hiring manager might join the call to discuss the role in more detail and answer any queries you might have about the position and the company. This call generally lasts around 30 minutes.

First-round Manager Interview

The hiring manager usually conducts the first round of interviews, which is slightly informal. This conversational interview aims to understand your expectations for the role and gauge how well you might fit into the team and company culture. Topics covered may include your previous work experiences, strengths, weaknesses, and how you handle stress and crisis situations.

Senior Manager Interview

If you pass the initial manager interview, you will move on to an interview with a senior manager. This interview will be more in-depth and focused on your past experiences and specific projects you’ve executed. Questions may include:

1. What are your weaknesses?
2. Describe a mistake you made in a past role.
3. How do you handle stress and crisis?

Technical Screening

In some interview processes, technical screening includes take-home exercises that assess your general data science knowledge and problem-solving abilities. Depending on the role’s specific requirements, these exercises may involve data analysis, machine learning problems, or coding exercises.

Onsite Interview Rounds

Finally, successful candidates will be invited to onsite interviews, which may involve one or more rounds with various team members and stakeholders. Depending on the role, you may be required to present your take-home assignment or discuss your approach to technical problems in more depth.

These rounds are designed to evaluate your technical expertise, problem-solving abilities, and how well you might integrate into the team. Expect questions on statistical methods, machine learning models, and possibly a coding exercise.

Typically, interviews at CGI vary by role and team, but commonly, Data Scientist interviews follow a fairly standardized process across these question topics.

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!