Quantitative Risk Lead

About the Team

Operations is the team that makes everything run. Within Operations, the Credit Risk team owns the models, frameworks, and day-to-day execution that keep Wealthsimple's brokerage business running soundly — across margin, delinquency, and dynamic risk reporting. We're a high-ownership team that works closely with Product, Finance, and the business to close gaps, reduce friction, and build for scale. If you take risk seriously and want your work to have a direct impact on how millions of Canadians experience their finances, you'll feel at home here.

About the Role

This is a hands-on Quantitative Risk Lead role to own end-to-end model development, deploy risk microservices, and manage production CI/CD execution pipelines. You won't rely on separate data engineering teams to productionize your work: you will build, containerize, test, and deploy your own production-grade quantitative code , while maintaining pipelines for model drift, data failures, and numerical anomalies. You will also lead the quantitative architecture for CIRO 5000 margin rules, stress testing, and VaR engines.

This role operates as a hybrid function: part Quantitative Financial Engineering, part Software Engineering/Data Science.

Key Responsibilities

• Quantitative Model Development: Design, code, and backtest Monte Carlo, Stressed, Historical, and Parametric VaR engines, CVaR, and multi-factor stress testing models.

• Derivatives Valuation & Pricing: Implement options pricing models (Black-Scholes-Merton, Binomial Trees), greeks execution engines, and shock scenarios across equities, options, futures, and fixed income.

• CIRO 5000 Regulatory Modeling: Operationalize CIRO 5000 margin rules into programmatic engines to compute stressed margin requirements, concentration haircuts, and firm capital impacts.

• Software Engineering & Pipelines: Write modular Python code ( NumPy , Pandas , Polars , SciPy ) and construct optimized SQL/dbt data pipelines for massive financial time-series datasets.

• CI/CD & Production Operations: Build and deploy Dockerized microservices via CI/CD workflows. Write unit tests ( pytest ), diagnose out-of-memory errors, and repair pipeline failures in real time.

• Governance & Leadership: Author technical methodology documentation for regulators and internal audit, maintain model registries (e.g., MLflow), and mentor/conduct code reviews for junior quants.

Requirements & Technical Qualifications

• Experience: 7–10+ years of quantitative development experience in financial services, preferably within a CIRO-regulated brokerage.

• Quantitative Finance: Expert knowledge of options pricing, volatility surfaces (SABR, SVI), market risk metrics, and CIRO 5000 margin/capital requirements.

• Software Engineering: Production experience with modern software engineering: Git, containerization (Docker), async processing, REST APIs (FastAPI), and testing frameworks ( pytest ).

• Data Stack: Strong SQL mastery and hands-on experience with cloud data warehouses (Snowflake, BigQuery, PostgreSQL) and distributed data tools.

• CI/CD & Cloud: Experience deploying models to cloud environments (AWS/GCP) via automated build and test pipelines.

• Leadership: Proven ability to lead technical projects, direct a small team, and manage backlog priorities.

Tech Stack

• Languages: Python, R, SQL

• Data & Storage: Snowflake, PostgreSQL, dbt

• DevOps & Infrastructure: Docker, Kubernetes, GitHub Actions, Airflow/Prefect

• Frameworks & Testing: pytest , FastAPI, MLflow

Nice to Have

• Education & Credentials: Master’s or Ph.D. in a quantitative field (Financial Engineering, Computer Science, Applied Math). CFA, FRM, CQF, or DFOL certification.

• System Design: Experience with event-driven architectures (Kafka) for real-time risk monitoring.