Operations Research Scientist
Full-Time Permanent
Downtown Toronto, ON (or Montreal, QC)
Hybrid - 3 days per week in-office
Large Enterprise Client
Role Overvie
w
In this position within a global decision‑science group, you’ll take open‑ended business challenges and shape them into analytical frameworks—building optimization or simulation models that guide leadership toward clearer, data‑driven choices. You’ll also be responsible for interpreting results in plain language, highlighting implications, tradeoffs, and recommended next step
s.
Key Contributi
ons
Problem Framing & Stakeholder Align
- mentWork closely with partners across the organization to understand their goals, boundaries, and measures of success. Capture any working assumptions and identify areas where information is incomplete or uncert
ain.Technique Selection & Model De
- signSelect modeling approaches—whether algorithmic optimizers, rule‑based heuristics, or scenario‑driven simulations—based on practical constraints and the context of the decision. Confirm model behavior using reasonableness checks and sensitivity explorat
ion.
Model Development & Sustai
- nmentBuild analytical tools that are robust enough for long‑term use and adaptable across multiple business areas. Collaborate with technical teams to ensure these solutions run efficiently and reliably in production environm
ents.
Insight Delivery & Decision S
- upportConvert analytical findings into clear, actionable guidance. Articulate the tradeoffs between options, advise on recommended actions, and outline how outcomes should be monitored after deplo
yment.
Innovation & Team Lea
- dershipContribute to the ongoing advancement of analytical methods within the team. Mentor peers, introduce new techniques when appropriate, and help elevate the organization’s approach to complex decision chal
lenges.
Background &
; Skills
Educational F
- oundationGraduate‑level preparation (or equivalent practical experience) in fields such as industrial engineering, operations research, applied math, or similar analytical dis
ciplines.
Modeling
- ExpertiseSolid understanding of optimization and simulation principles, with a track record of applying them to real‑world business situations. Able to articulate why a particular technique was chosen and where it has li
mitations.
Communicatio
- n StrengthsProven ability to translate technical outputs into insights that make sense to audiences without a technical
background.
Technical
- ProficiencySkilled in Python and familiar with optimization tools such as Gurobi, CPLEX, or comparabl
e platforms.
