What if you could redefine what's possible?
With us, you can. You want Purpose. Growth. Opportunity. People who get it.
We are the home of ambitious, passionate, and innovative world shapers.
With an unmatched breadth and depth of engineering, advisory and science based expertise, our global minds unite to power local solutions.
We are pathfinders and impact makers.
We are Visioneers.
We are WSP.
The Opportunity
Your impact starts here. As a Quantitative Risk Analytics Specialist, you will apply advanced quantitative methods to help shape safe, resilient and sustainable outcomes across mining, resources, infrastructure and environmental projects. This role can be based anywhere in Canada.
You will lead and deliver complex risk analytics assignments, developing probabilistic models, simulations and decision-support tools that help our clients understand uncertainty, interdependencies and potential impacts on their assets and operations. Partnering with multi-disciplinary teams across WSP, you will turn data and domain knowledge into insights that support risk management strategies, regulatory compliance and critical investment and operational decisions.
Your Impact
In this role, you will:
- Lead and deliver quantitative risk analytics for mining, resource, infrastructure and environmental projects, from scoping and data collection through to modeling, interpretation and recommendations.
- Develop and implement probabilistic models and simulations (including Monte Carlo-based approaches) to quantify uncertainty, evaluate risk exposure and support decision-making for complex engineering and operational systems.
- Apply and tailor risk modeling methodologies such as Fault Tree Analysis (FTA), Event Tree Analysis (ETA) and Quantitative Risk Assessment (QRA) to assess safety, environmental and operational hazards across projects.
- Design and run scenario and sensitivity analyses to test risk response options, mitigation strategies and investment choices under different assumptions and future conditions.
- Translate engineering and operational problems into quantitative models, clearly documenting assumptions, data sources, limitations and implications for decision-makers.
- Work closely with technical specialists and project managers across WSP to embed risk analytics into broader project frameworks, linking to design, environmental assessment, permitting, cost and schedule, and asset management.
- Prepare clear, concise reports, visualizations and presentations that communicate complex analytical findings to both technical and non-technical audiences, including clients and senior stakeholders.
- Facilitate and contribute to multi-disciplinary risk workshops, guiding subject matter experts through structured risk identification, evaluation and prioritization processes.
- Support the development and continuous improvement of internal tools, templates and best practices for quantitative risk analytics, and provide mentorship on methods and approaches where appropriate.
The Skills That Set You Apart
You bring a strong blend of technical depth, domain awareness and practical problem-solving, including:
- 5-10 years of experience in risk analytics, ideally applied within mining, infrastructure, energy, resource or environmental risk domains.
- A Master's degree or PhD (or equivalent experience) in Statistics, Applied Mathematics, Data Science, Engineering or a related field, with a strong foundation in probability, statistics and uncertainty analysis.
- Demonstrated experience with Monte Carlo simulation and probabilistic risk modeling, including scenario and sensitivity analysis for complex systems.
- Experience modeling interdependencies between risks and system components.
- Familiarity with risk modeling methodologies such as FTA, ETA and QRA, and the ability to adapt them to different project and regulatory contexts.
- Proficiency in Python or R for data analysis and modeling, including building reproducible analytical workflows and leveraging relevant statistical and simulation libraries.
- Experience working with structured and unstructured data to support quantitative modeling and analytics
- Proven ability to translate engineering or operational questions into quantitative models, and to clearly explain model logic and results to diverse audiences.
- Experience working with and guiding diverse teams of subject matter experts and facilitating workshops or risk sessions in multi-stakeholder environments.
- Strong skills in communicating analytical insights, ideally supported by data visualization tools (e.g., Power BI, Tableau or Python visualization libraries).
- An asset (nice to have):
- Experience with probabilistic modeling of risk interdependencies.
- Knowledge of machine learning, predictive analytics and optimization methods in a risk context.
- Exposure to financial risk modeling or cost-risk analysis, linking risk outcomes to business cases and investment decisions.
- Knowledge of reliability enginee