Manager, Software Developer - Quantitative Market Risk
Wealthsimple Technologies · Full-time · Posted 1w ago
Wealthsimple is Canada’s leading financial innovator. The company offers a full suite of simple, sophisticated financial products across managed investing, do-it-yourself trading, cryptocurrency, tax filing, spending and saving. Wealthsimple currently serves more than 4 million Canadians and holds over $155 billion in assets under administration. The company was founded in 2014 by a team of financial experts and technology entrepreneurs, and is headquartered in Toronto, Canada.
We're proud of what we've built — and we're just getting started. Read our Culture Manual and learn more about how we work.
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.
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.
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.
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.
Languages: Python, R, SQL
Data & Storage: Snowflake, PostgreSQL, dbt
DevOps & Infrastructure: Docker, Kubernetes, GitHub Actions, Airflow/Prefect
Frameworks & Testing: pytest, FastAPI, MLflow
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.
🌸 Top-tier health benefits and life insurance
📈 Long-term group savings with employer match, through Wealthsimple for Business
🌴 20 vacation days, 4 wellness days, and unlimited sick and mental health days per year
✈️ 90 days away: work outside Canada for up to 90 days per year
👥 Employee resource groups, including Rainbow (2SLGBTQ), Women of WS, and Black at WS
🌎 We are a hybrid team with over 1,500 employees across North America. The people are one of the best parts of working here: you'll collaborate with incredibly talented, curious, and driven teammates who are deeply committed to doing great work.
Technology & Innovation at Wealthsimple: We move quickly and build thoughtfully. That means we're always looking for better ways to work — whether that's new tools, AI, or rethinking how we approach a problem. We don't expect you to have all the answers, but we do expect curiosity and a willingness to evolve alongside the products we're building.
Inclusion Statement: We're building products for a diverse world, and we need a diverse team to do it well. We strongly encourage applications from everyone, regardless of race, religion, colour, national origin, gender, sexual orientation, age, marital status, or disability status.
Accessibility Statement: We're committed to an accessible hiring experience. If you need any accommodations throughout the interview process, please let us know — we'll work with you to make sure you have what you need. We also welcome any feedback on how we can better accommodate candidates with accessibility needs.
AI in Hiring: We may use artificial intelligence (AI) tools to support parts of our hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our team but don't replace human judgment – all final hiring decisions are made by people. If you have questions about how your data is used, reach out to us.
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