Python Risk Developer

IO TECH SOLUTIONS LIMITED; Hong Kong; On-site; HKD 900,000 - 1,700,000; Full time

Job summary

IO TECH SOLUTIONS LIMITED is seeking a Python Risk Developer to design, build, and maintain core risk infrastructure used by senior management and portfolio managers across multiple asset classes. You will implement risk models, backtest them, and ensure tight integration with trading systems.

The role focuses on high-performance Python, data pipelines, and scalable risk tooling, with collaboration across Quant Researchers and Risk Managers.

Qualifications

• Strong background in Python development for risk or trading systems.
• Experience implementing VaR, stress tests, and scenario analysis.
• Hands-on with large datasets and low-latency data pipelines.

Responsibilities

• Design, build, and maintain Python-based risk engines and pricing libraries.
• Backtest and productionalize mathematical risk models across asset classes.
• Develop frameworks for multi-asset risk aggregation and data pipelines.
• Tune performance to minimize latency in intra-day and end-of-day reporting.

Skills

Python

Risk modeling

Quant research

Data pipelines

Performance optimization

VaR & stress testing

Cross-asset risk

Tools

NumPy

Pandas

Job description

As a \"Python Risk Developer\", you will sit at the intersection of quantitative research, risk management, and software engineering. You will be responsible for designing, building, and maintaining the core pricing and risk infrastructure used by senior management and portfolio managers across multiple asset classes (including equities, macro, and credit). You will ensure that our global risk models are accurate, highly performant, and deeply integrated into our daily trading lifecycle.

Key Responsibilities:

• Risk Infrastructure Engineering: Design, build, and optimize robust Python-based risk engines, pricing libraries, and data pipelines to support intra-day and end-of-day risk reporting.
• Model Implementation: Collaborate closely with Quantitative Researchers and Risk Managers to implement, backtest, and productionalize mathematical risk models (e.g., VaR, stress testing, scenario analysis, and factor models).
• Cross-Asset Support: Develop framework features capable of handling a diverse set of financial instruments, ensuring seamless risk aggregation across regional portfolios.
• Performance Tuning: Optimize data processing pipelines handling massive datasets, using tools like NumPy, Pandas, or parallel computing frameworks to minimize latency.
• System Integration: Integrate risk tools with internal portfolio management platforms, order management systems (OMS), and external market data providers.