EndeavourOn is looking for a Master's student in Data Science or Data Engineering to join us in Saint John, NB, for a hands‑on internship at the intersection of data engineering and applied AI.
Quoting is where margin is won or lost. Our quoting tool produces the pricing that eventually becomes an invoice — and when raw material costs, supplier rates, or market conditions shift between the quote and the invoice, margin quietly erodes. Your job is to help us see that coming and correct for it.
You'll work with years of historical quote data, build the pipelines that make it usable, and help develop AI models that adjust pricing for market fluctuation so that no quote leaves the door undervalued.
What you'll do
- Collect, clean, and structure historical business quote data — pricing, cost inputs, margins, win/loss outcomes, and customer segments.
- Build and maintain Python data pipelines in PyCharm, with an emphasis on reproducibility, version control, and clear documentation.
- Explore the data for the patterns that matter: where margin compresses, which quote types are most exposed to cost volatility, and how pricing drifts between quote and invoice.
- Develop and test AI/ML models that account for market fluctuation in price and cost inputs, and translate those signals into pricing and margin adjustments inside the quoting tool.
- Build guardrails that flag quotes falling below acceptable margin thresholds before they're issued, so generated invoices reflect true business value.
- Validate model output against historical results and work with our commercial team to sanity‑check recommendations against real‑world judgment.
- Present findings and recommendations to both technical and non‑technical stakeholders.
What we're looking for
- Currently enrolled in a Master's program in Data Science, Data Engineering, Analytics, Computer Science, or a related field.
- Strong Python skills, with practical experience in PyCharm and libraries such as pandas, NumPy, and scikit‑learn.
- Solid grounding in SQL and relational data modelling.
- Experience with machine learning fundamentals — regression, time series forecasting, model evaluation, and the discipline to know when a model shouldn't be trusted.
- Comfort working with messy, incomplete, real‑world business data.
- Clear communication skills, and genuine curiosity about the business problem behind the numbers.
- Legally eligible to work in Canada for the duration of the internship.
Nice to have
- Exposure to pricing analytics, margin analysis, quoting/CPQ systems, or ERP data.
- Familiarity with time series forecasting for cost or commodity indices.
- Experience with version control (Git), cloud data platforms, or workflow orchestration tools.
- Interest in the practical side of AI — models that get used, not just models that get published.
What you'll gain
- Ownership of a defined, measurable problem with visible impact on the business.
- Mentorship from our data and commercial teams.
- Exposure to the full lifecycle: raw data → pipeline → model → production decision.
- Experience that maps directly onto a thesis, capstone, or research project.