Associate Director, Climate Risk Modeling Specialist

What is the opportunity?

Global Risk Analytics (GRA) is the center of expertise for models used in risk management, covering market risk, counterparty credit risk, and emerging risk. The GRA Data Analytics and Machine Learning (DAML) team focuses on innovations and building capabilities to model emerging risks, including climate analytics and machine learning. To support RBC's various climate risk scenario analysis and stress testing mandates, GRA continues to develop expertise in climate risk analytics.

Given the increasing importance of assessing the impacts of climate change on financial institutions, GRA is developing enterprise-wide and regional climate risk scenario analysis models. These models cover both physical risk and transition risk to comply with regulatory guidelines such as OSFI's B-15 and PRA's SS3/19 along with other relevant regional regulations.

GRA is seeking an individual who is an expert in climate physical risk analysis and is passionate about applying innovative data analytics and strong programming skills to enhance our climate risk analytics capabilities.

What will you do?

• Develop a detailed physical risk scenario analysis framework using the hazards, vulnerabilities, and exposure (HVE) approach.
• Collaborate with vendor-provided hazard models to integrate and validate their outputs, while conducting comprehensive benchmarks using open-source hazard datasets.
• Create specialized analytics for specific hazards such as wildfire, tropical cyclones, flooding, etc.
• Enhance, maintain, and operate existing physical risk and transition risk models.
• Design and implement Python-based tools at a production level.

What do you need to succeed?

Must-have

• PhD or Master's degree in Engineering, Computer Science, Statistics, or related field.
• Proficiency with hazard modeling and meteorological modeling.
• Hands-on experience with RCP (Representative Concentration Pathways), SSP (Shared Socioeconomic Pathways) scenarios, and CMIP5/6 datasets.
• Solid statistical and mathematical knowledge with attention to details.
• Proficient in Python with at least 3 years' experience.

Nice-to-have

• Proficiency in Geographic Information Systems (GIS) software such as QGIS.
• Experience working with geographical data structures like NetCDF, Raster, Shape files.
• Familiarity with geographic data processing packages including Fiona, GeoPandas, Shapely, Rasterio, Xarray, GDAL, etc.
• Knowledge of machine learning, data analytics, and data visualization.
• Proficiency in AI/ML libraries such as PyTorch, Lightning, scikit-learn, statsmodels, etc.
• Ability to communicate technical matters clearly both verbally and in writing.
• Team players to collaborate and innovate.
• Understanding of the overall risk function within banks.
• Knowledge of financial products in banking book and trading book.

What's in it for you?

We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

• Leaders who support your development through coaching and managing opportunities.
• Ability to make a difference and lasting impact.
• Work in a dynamic, collaborative, progressive, and high-performing team.
• Opportunities to do challenging work and make a difference.
• Opportunities to building close relationships.

Job Skills

Additional Job Details

Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above