Intern, Supply Chain (Data Insights and Profitability), (Winter 2027)
Bombardier is hiring an Intern, Supply Chain (Data Insights and Profitability), (Winter 2027). Level rates it ; you can apply on Bombardier.
Level's read on this job
- Level rates this role Builds AI ●●●●: the job is building AI systems.
- The posting mentions PyTorch, TensorFlow and scikit-learn among its tools and skills.
- Level classes it as an internship.
- Bombardier has 1,000+ other open roles on Level.
- Posted within the past two weeks.
AI in this role
Innovation is in our DNA… is it in yours?
Bombardier is a global leader in aviation, creating innovative and game-changing planes. Our products and services provide world-class experiences that set new standards in passenger comfort, energy efficiency, reliability and safety. We are a global organization focused on working together with a team spirit.
Your boarding pass will include…
Several conferences, including:
- Meet An Executive
- Women Taking Flight
Learning more about Bombardier, including:
- Bombardier Products conference
- Visits of the Bombardier sites
- Bombardier Academy of Learning
Many social/networking opportunities, including:
- Volunteering
- Networking for Success
- 5 à 7, Potluck, and much more!
What You Will Learn
- Gain experience in applying AI and ML in a real-world business setting.
- Learn about supply chain management and the aerospace industry.
- Learn from experienced professionals in applying analytics in the workplace.
- Be exposed to management to learn about their business priorities.
- Learn about financial concepts that are important for managing a global business.
What are your contributions to the team?
- Utilize ERP data and data science techniques to develop demand forecasting models, enabling the company to anticipate customer demand, optimize inventory levels, and streamline procurement processes.
- Leverage Power BI for data visualization and reporting.
- Apply machine learning algorithms to identify patterns and predict likely errors in invoices, ordering policies, and budgets enabling the company to reduce rework and improve financial accuracy.
- Hands-on pattern recognition and anomaly detection ML solutions.
- Collaborate with stakeholders to construct customer-facing reporting tools that provide insights and metrics tracking forecast accuracy, inventory levels, transportation efficiency, and other key performance indicators (KPIs).
- Manage the development of tools to increase efficiencies throughout the team.
- Present findings in a clear and compelling manner to stakeholders.
- Work in members of upper management to diagnose problems, identify business needs, and develop solutions and tools that meet their needs.
How to thrive in this role?
As our ideal candidate:
- You are studying a degree in data science, business analytics, computer science, mathematics, statistics, engineering or a related field.
- You have at least 2 years of experience in developing and deploying machine learning models in an academic setting.
- Strong programming skills in languages such as SQL, Python, R, or similar, with a focus on developing scalable and efficient AI solutions.
- Demonstrated academic or project experience working as a data scientist, AI engineering, or in a similar role, with a strong focus on developing AI tools and algorithms would be a strong asset.
- You have strong knowledge of machine learning algorithms, statistical modeling, and data analysis techniques at the undergraduate level.
- You have hands-on experience with programming languages such as Python, Pandas, R, SQL: Experience with complex queries.
- You have In-depth understanding of machine learning algorithms, techniques, and frameworks. Candidates should be proficient in designing, training, and evaluating machine learning models using tools such as TensorFlow, PyTorch, scikit-learn, etc.
- You have experience in development using cloud providers Azure (Databricks, Synapse) and AWS.
- You have some knowledge of Pyspark and AzureDevops or equivalents for CICD orchestration.
- You have experience at some level with some production-level ML lifecycle management, at least with MLflow.
- You have strong communication and interpersonal skills to work effectively with cross-functional teams.
- Familiarity with Microsoft Access and SAP and Reinforcement Learning is considered an asset.
- You are able to work effectively in English and French on a daily basis.
- You are able to work onsite at the designated Montreal office location, at least two (2) days per week, sometimes more, as per the applicable policy.
Boarding Information:
- Location: Administrative Centre (CA)
- Duration: 4 months
- Flexible workplace-Hybrid
It is important to note that our internship opportunities are open to students only, not new graduates. All our interns may be required to occasionally travel outside of Canada for training/work purpose.
How we rate this
Intern, Supply Chain (Data Insights and Profitability), (Winter 2027) at Bombardier rates 86 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- What's a project where you used PyTorch hands-on?
- Walk me through how you've used TensorFlow in your day-to-day work.
- What are the limits of scikit-learn that you've run into, and how did you work around them?
- What's a project where you used MLflow hands-on?
- Walk me through how you've used Databricks in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: PyTorch, TensorFlow, scikit-learn, MLflow, and Databricks. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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