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Wealthsimple is seeking a hands-on Manager of Quantitative Market Risk to lead end-to-end model development, deploy risk microservices, and manage production CI/CD execution pipelines for the Credit Risk team. This hybrid quantitative finance and software engineering role owns the models, frameworks, and day-to-day execution that keep Wealthsimple's brokerage business running soundly across margin, delinquency, and dynamic risk reporting.
Key responsibilities include designing, coding, and backtesting Monte Carlo, Stressed, Historical, and Parametric VaR engines, CVaR, and multi-factor stress testing models. You will implement derivatives valuation and pricing models (Black-Scholes-Merton, Binomial Trees), greeks execution engines, and shock scenarios across equities, options, futures, and fixed income. The role requires operationalizing CIRO 5000 margin rules into programmatic engines to compute stressed margin requirements, concentration haircuts, and firm capital impacts.
You will write modular Python code (NumPy, Pandas, Polars, SciPy) and construct optimized SQL/dbt data pipelines for massive financial time-series datasets. You'll 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. Leadership responsibilities include authoring technical methodology documentation for regulators and internal audit, maintaining model registries (e.g., MLflow), and mentoring/conducting code reviews for junior quantitative staff.
Required qualifications: 7–10+ years of quantitative development experience in financial services, preferably within a CIRO-regulated brokerage. Expert knowledge of options pricing, volatility surfaces (SABR, SVI), market risk metrics, and CIRO 5000 margin/capital requirements. Production experience with modern software engineering: Git, containerization (Docker), async processing, REST APIs (FastAPI), and testing frameworks (pytest). Strong SQL mastery and hands-on experience with cloud data warehouses (Snowflake, BigQuery, PostgreSQL) and distributed data tools. Experience deploying models to cloud environments (AWS/GCP) via automated build and test pipelines. Proven ability to lead technical projects, direct a small team, and manage backlog priorities.
Tech stack: Python, R, SQL; Snowflake, PostgreSQL, dbt; Docker, Kubernetes, GitHub Actions, Airflow/Prefect; pytest, FastAPI, MLflow. Nice-to-have: Master's or Ph.D. in quantitative field; CFA, FRM, CQF, or DFOL certification; experience with event-driven architectures (Kafka) for real-time risk monitoring.
