From AMA’s careers page.
PREDICTING WHAT HAPPENS NEXT IS JUST THE beginning OF YOUR JOURNEY.
The Opportunity
If building models that help people make better decisions, understanding uncertainty, and turning complex systems into meaningful predictions sounds like your kind of challenge, we’re looking for you.
As an Architecture Research Intern, you’ll use simulation and queueing theory to understand how business changes (new service channels, process redesigns, demand shifts) ripple through operations and shape the customer experience. You’ll contribute to initiatives in Queue Management, Estimated Wait Time, and Predictive Simulation. From forecasting wait times to modelling the dynamics of complex operational systems, you’ll help develop solutions that improve experiences for our members.
Your background might be in queueing theory, simulation science, applied mathematics, stochastic processes, differential equations, or optimization. Either way, you’ll apply your research skills to real-world problems where uncertainty isn’t a complication, it’s part of the solution.
*This role is for a 12-month opportunity with the possibility of extension.*
The Team You’ve Been Looking For
AMA is more than a workplace; it’s a family. Together, we help protect the things that matter most to our more than one-million members—everything from roadside safety to retail savings, food security to childhood well-being. And with operations that span automotive, registries, travel, insurance, and more, the opportunities to grow with us are almost limitless!
Building on a century of proud service in Alberta communities, we’re passionate about making a meaningful difference in the places where we live and work. And that all begins with our people. As part of the AMA team, you’ll enjoy:
- Competitive wage
- Employee-exclusive discounts on travel, insurance, memberships, and more
- Ongoing professional development
- Vacation earnings paid out with each pay period
- Flexible work models that makes sense for your lifestyle
For us, curiosity fuels innovation. We value people who are eager to explore new ideas and who are comfortable putting emerging technologies into practice. We see experience with AI‑powered tools like ChatGPT, Copilot, and similar platforms as a real plus - especially when it’s used to work smarter, increase efficiency, and find better ways to get things done.
Who You Are
- You’re fascinated by uncertainty and enjoy understanding how complex systems behave over time.
- You see queues everywhere and naturally think in terms of arrivals, service, congestion, and waiting.
- You naturally think in terms of probabilities, outcomes, and trade-offs.
- You ask “what if we changed this?” and know how to build a model that answers it rigorously.
- You’re comfortable working with incomplete information and building models that help make sense of it.
- You can balance model fidelity against computational cost and decision deadlines.
- You communicate technical concepts clearly and can explain uncertainty without creating confusion.
- You’re analytical, curious, and motivated by solving real-world problems.
- You enjoy collaborating and know the best solutions come from combining different perspectives.
- You love turning research ideas into practical models that create measurable value.
How You’ll Make an Impact
- Contribute to predictive models and simulation frameworks that support queue management and estimated wait-time forecasting.
- Apply queueing theory to model customer flow, congestion, and waiting (for example multi-server queues, priority queues, queues with abandonment and retrials, and queueing networks) and use it to derive fast, interpretable wait-time estimates.
- Build simulation models (discrete-event, agent-based, and/or system dynamics) to evaluate how proposed business changes affect wait times, service levels, abandonment, and overall customer experience before they are implemented.
- Compare analytical queueing results against detailed simulation and real operational data to determine where theory holds, where it breaks down, and which approach best suits each decision.
- Formulate and analyze continuous-time models of system behavior using ordinary and stochastic differential equations (for example fluid and diffusion approximations of queues, including heavy-traffic limits based on reflected Brownian motion), and validate them against detailed simulation and real operational data.
- Design and apply time-constrained optimization algorithms (such as simulation-based optimization, parameter tuning, and anytime or heuristic methods) that deliver good decisions within real operational time limits.
- Design, test, and refine models that capture uncertainty, customer behavior, and system dynamics.
- Apply probabilistic and statistical techniques to evaluate performance, calibrate and validate models, and improve predictive accuracy.
- Run scenario, sensitivity, and what-if analyses that help business leaders understand bottlenecks, trade-offs, and future outcomes.
- Analyze results and communicate key insights, assumptions, limitations, and recommendations to stakeholders.
- Collaborate with researchers, engineers, and business teams to turn theory into practical, deployable solutions.
- Explore innovative approaches that improve decision-making across AMA’s products, services, and operations.
What You Bring to the Table
- You’re currently pursuing, or have completed, an MSc or PhD in Mathematics, Applied Mathematics, Physics, Statistics, Operations Research, Computational Science, or a closely related quantitative discipline.
- You have in-depth knowledge and hands-on experience of queueing theory, including Markovian and general queues (M/M/c, M/G/1, G/G/c), Little’s Law, queues with abandonment and priorities, queueing networks, transient and steady-state analysis, and heavy-traffic and diffusion approximations. You can fit queueing models to real data and judge when their assumptions hold.
- You have in-depth knowledge of simulation science, including discrete-event and/or agent-based simulation, Monte Carlo methods, variance reduction, input modelling, model verification and validation, and experimental design for simulation studies.
- You have a strong foundation in differential equations (ordinary, partial, and/or stochastic), including Brownian motion and diffusion processes, Fokker-Planck equations, and numerical methods for SDEs, and can apply them to model the dynamics of real systems.
- You have working knowledge of optimization, particularly time-constrained or real-time settings: simulation optimization, stochastic and dynamic programming, metaheuristics, or anytime algorithms.
- You have a strong foundation in probability and stochastic processes, with experience in one or more of the following:
- Markov chains and Markov decision processes
- Renewal and point processes (for example Poisson and non-homogeneous Poisson arrival processes)
- Statistical mechanics
- Stochastic differential equations
- You have strong statistical judgment and understand the nuances of uncertainty, including the distinction between prediction intervals and confidence intervals.
- You’re proficient in Python and can independently translate models and research concepts into reliable, well-tested code (for example NumPy, SciPy, SimPy, and optimization libraries).
Belonging Matters Here
AMA is committed to providing an inclusive, respectful, and accessible environment. If you require an accommodation at any time during our recruitment process, please contact recruitment@ama.ab.ca. We’ll work with you to feel supported throughout your journey with us.
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WORK MODEL:
Remote
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We thank all applicants for their interest; however, only those selected for an interview will be contacted.
