CGI Data Scientist interview typically runs 3 rounds: technical phone screen, in-office technical case, hiring manager behavioral. It usually takes about 1-2 weeks and is notably case-based with no programming.
$104K
Avg. Base Comp
$160K
Avg. Total Comp
3
Typical Rounds
1-3 weeks
Process Length
Our candidates’ experience suggests CGI cares less about flashy modeling and more about whether you can turn an ambiguous client problem into a sensible consulting plan. In the use-case discussion, the strongest response wasn’t a technical deep dive; it was the ability to separate business understanding, revenue drivers, cost factors, and the data needed to support each. That tells us CGI is looking for people who can frame the problem before solving it and speak in terms a client can act on.
A recurring theme is that the company seems to value practical judgment over complexity. The scenario around shipment and transit revenue was intentionally open-ended, and the candidate who did well focused on metrics like revenue per customer and profit, then reasoned through how each component could be improved or reduced. That’s a strong signal that CGI wants candidates who can connect analysis to business levers, not just produce an answer. We’ve also seen that the technical conversation can stay high level, so clarity of thought matters more than coding theatrics.
The behavioral portion appears to be about fit for a client-facing consulting environment. Since CGI positions itself as a partner and services provider, our read is that interviewers are checking for structured communication and stakeholder maturity: can you explain tradeoffs, stay grounded in the client’s industry, and sound credible without overcomplicating the problem? Candidates who show that balance tend to come across as ready for CGI’s style of work.
Synthetized from 1 candidates reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Cgi process.
Three rounds:
30-minute technical phone call going through my CV.
1-hour in-office technical use case, with no programming. The case was a high-level scenario: imagine we are working with a client in the shipment and transit industry and want to help them improve revenue. The interviewer asked how I would approach this not-well-defined problem, what data I would need, what metrics I would track, and what the client could improve.
I broke the problem down into business understanding, understanding revenue and its components, cost factors, and the data we could acquire. I then explained revenue-related metrics that we could track over time, such as revenue per customer and profit. From there, I explored hypothetical ways each component could be minimized if it was a cost or maximized if it was a revenue stream.
30-minute in-office interview with the hiring manager, focused on behavioral questions.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Cgi
Find the average yearly purchases for each product
| Question | |
|---|---|
| Prime to N | |
| Sum to N | |
| Hurdles In Data Projects | |
| Find the First Non-Repeating Character in a String | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| 2nd Highest Salary | |
| Employee Salaries | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| Merge Sorted Lists | |
| Closest SAT Scores | |
| Largest Salary by Department | |
| First Touch Attribution | |
| Experiment Validity | |
| First to Six | |
| SELECTive Wine Connoisseur | |
| Manager Team Sizes | |
| Bagging vs Boosting | |
| Raining in Seattle | |
| Find the Missing Number | |
| 500 Cards | |
| Top 5 Turnover Risk | |
| Over-Budget Projects | |
| P-value to a Layman | |
| Size of Joins | |
| Swipe Precision | |
| Encoding Categorical Features | |
| Top 3 Users |
Synthesized from candidate reports. Individual experiences may vary.
A recruiter or technical interviewer reviews your CV and walks through your background. Expect questions about your past projects, technical experience, and how your experience fits the Data Scientist role.
You work through a high-level, non-coding business case in person. In the reported experience, the scenario involved helping a shipment and transit client improve revenue, and the interviewer asked how you would structure the problem, what data you would need, which metrics you would track, and what actions the client could take.
A final in-office behavioral interview with the hiring manager. The discussion focuses on your motivation, collaboration style, and overall fit for the team and consulting environment.