Stantec Machine Learning Engineer Interview Questions + Guide in 2025

Stantec is a global leader in sustainable design and engineering solutions, dedicated to fostering innovation while delivering exceptional client service.

As a Machine Learning Engineer at Stantec, you will play a pivotal role in developing and implementing machine learning models to solve complex problems in various domains, such as environmental management, infrastructure development, and urban planning. Key responsibilities include designing algorithms, analyzing data sets, and optimizing model performance to enhance decision-making processes. A successful candidate will possess strong programming skills in Python and SQL, a solid understanding of machine learning principles, and experience with statistical analysis. Additionally, effective communication skills and the ability to collaborate with cross-functional teams will be essential, aligning with Stantec's commitment to teamwork and client-focused solutions.

This guide will help you prepare for the interview process by providing insight into the role's expectations and the skills needed to stand out as a candidate at Stantec.

The interview process for a Machine Learning Engineer at Stantec is designed to assess both technical skills and cultural fit within the organization. It typically unfolds over several stages, allowing candidates to engage with various team members and showcase their expertise.

1. Initial Contact

The process begins with an initial outreach from the HR representative, who will typically contact you via email to schedule a phone interview. This preliminary conversation is generally brief, lasting around 10-15 minutes, and focuses on your background, availability, and general fit for the role. The HR representative may also discuss salary expectations to ensure alignment before proceeding further.

2. Technical Screening

Following the initial contact, candidates usually undergo a technical screening, which may be conducted via video call. This interview often involves discussions with a hiring manager or a senior technical staff member. Expect questions that assess your understanding of machine learning concepts, algorithms, and relevant programming languages such as Python. You may also be asked to explain your past projects and how they relate to the role.

3. Behavioral Interview

After the technical screening, candidates typically participate in a behavioral interview. This stage may involve multiple interviewers, including project managers and team leads. The focus here is on understanding your work style, problem-solving abilities, and how you handle challenges in a team environment. Be prepared to discuss your project management experience and how you envision your career path aligning with Stantec's goals.

4. Assessment Task

In some cases, candidates may be required to complete an assessment task. This could involve presenting a machine learning project or analysis you have conducted in the past. While the task may be challenging, it is an opportunity to demonstrate your analytical skills and thought process. Feedback is often provided after this stage, which can be valuable for your growth.

5. Final Interview

The final interview stage usually involves a more in-depth discussion with senior management or executives. This is your chance to ask questions about the company culture, future projects, and how you can contribute to the team. Expect to discuss your long-term career aspirations and how they align with Stantec's vision.

As you prepare for your interviews, consider the types of questions that may arise during the process.

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Stantec. The interview process is likely to cover a range of topics, including technical skills, project management experience, and cultural fit within the company. Candidates should be prepared to discuss their background, relevant projects, and how they envision their career path aligning with Stantec's goals.

Technical Skills

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the types of problems each approach is best suited for.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customers based on purchasing behavior.”

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.

How to Answer

Outline the project scope, your role, the challenges encountered, and how you overcame them. Emphasize the impact of your work.

Example

“I worked on a predictive maintenance project for industrial equipment. One challenge was dealing with imbalanced data, which I addressed by implementing SMOTE to generate synthetic samples. This improved our model's accuracy significantly, leading to a 20% reduction in downtime.”

3. What techniques do you use for feature selection?

Feature selection is critical for building efficient models.

How to Answer

Discuss various techniques such as recursive feature elimination, LASSO regression, or tree-based methods. Explain why feature selection is important.

Example

“I often use recursive feature elimination combined with cross-validation to identify the most impactful features. This not only improves model performance but also reduces overfitting, making the model more interpretable.”

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

Handling missing data is a common challenge in data science.

How to Answer

Explain different strategies for dealing with missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I typically assess the extent of missing data first. If it’s minimal, I might use mean or median imputation. For larger gaps, I consider using algorithms like k-NN that can handle missing values directly or even create a separate category for missing data.”

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

Overfitting is a critical concept in machine learning that candidates should understand.

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 cross-validation to ensure the model generalizes well, and I apply regularization methods like L1 or L2 to penalize overly complex models.”

Project Management

1. Describe your experience with project management in a technical environment.

This question gauges your ability to manage projects effectively.

How to Answer

Discuss your project management experience, including methodologies used (e.g., Agile, Scrum) and how you ensure project success.

Example

“I have managed several machine learning projects using Agile methodologies. I prioritize tasks through sprints, ensuring that the team remains focused on delivering incremental value while adapting to changes in project scope.”

2. How do you prioritize tasks when managing multiple projects?

Prioritization is key in project management.

How to Answer

Explain your approach to prioritization, including any frameworks or tools you use.

Example

“I use the Eisenhower Matrix to categorize tasks based on urgency and importance. This helps me focus on high-impact activities while delegating or postponing less critical tasks.”

3. Can you give an example of a project that did not go as planned? What did you learn?

This question assesses your ability to learn from setbacks.

How to Answer

Share a specific example, focusing on the lessons learned and how you applied them to future projects.

Example

“In one project, we underestimated the data cleaning phase, which delayed our timeline. I learned the importance of thorough initial assessments and now always allocate extra time for data preparation in future projects.”

4. How do you ensure effective communication within your team?

Communication is vital for project success.

How to Answer

Discuss your strategies for maintaining clear communication, such as regular meetings or collaboration tools.

Example

“I schedule weekly stand-up meetings to discuss progress and roadblocks, and I use tools like Slack for real-time communication. This ensures everyone is aligned and can address issues promptly.”

5. What project management tools are you familiar with?

Familiarity with tools can enhance project efficiency.

How to Answer

List the project management tools you have used and how they contributed to project success.

Example

“I have experience with tools like Jira for tracking project progress, Trello for task management, and Asana for team collaboration. These tools help streamline workflows and improve accountability within the team.”

Cultural Fit

1. Why do you want to work at Stantec?

Understanding your motivation for joining the company is important.

How to Answer

Express your alignment with Stantec’s values and mission, and how you see yourself contributing to their goals.

Example

“I admire Stantec’s commitment to sustainable development and innovation in engineering. I believe my background in machine learning can contribute to projects that enhance environmental sustainability, aligning perfectly with the company’s mission.”

2. Where do you see yourself in five years?

This question assesses your career aspirations and alignment with the company.

How to Answer

Discuss your career goals and how they relate to the opportunities at Stantec.

Example

“In five years, I see myself in a leadership role within the machine learning team, driving innovative projects that leverage data to solve complex engineering challenges. I’m excited about the potential for growth at Stantec and contributing to impactful projects.”

3. How do you handle feedback and criticism?

This question evaluates your ability to grow and adapt.

How to Answer

Share your perspective on feedback and provide an example of how you’ve used it constructively.

Example

“I view feedback as an opportunity for growth. For instance, after receiving constructive criticism on a project presentation, I sought additional training in data visualization, which significantly improved my future presentations.”

4. Describe a time when you had to work with a difficult team member. How did you handle it?

This question assesses your interpersonal skills and conflict resolution abilities.

How to Answer

Provide a specific example, focusing on your approach to resolving the conflict and maintaining team cohesion.

Example

“I once worked with a team member who was resistant to collaboration. I initiated a one-on-one conversation to understand their perspective and found common ground. By fostering open communication, we were able to work together more effectively and complete the project successfully.”

5. What do you think is the most important quality for a Machine Learning Engineer at Stantec?

This question gauges your understanding of the role and company culture.

How to Answer

Identify a quality that aligns with Stantec’s values and the demands of the role.

Example

“I believe adaptability is crucial for a Machine Learning Engineer at Stantec. The field is constantly evolving, and being open to learning new techniques and technologies will enable us to deliver innovative solutions that meet our clients’ needs.”