Head of Quant & Risk Analytics

Role Overview:
- Build and lead a high-performing middle-office team spanning four domains: commercial analytics, market-making & institutional analytics, trading risk, and fraud & detection.
- The team does not own a trading book; instead, it enables every trading desk to perform measurably better.
- Responsible for hiring, developing, and retaining the team, owning the firm’s risk and performance infrastructure, and acting as the independent analytical authority to desk heads and senior leadership across a distributed, multi-site organisation.

Key Responsibilities:
- Design the team structure and lead 5–10 quant researchers across four domains.
- Establish the quantitative and analytics stack as the single source of truth for the firm’s commercial and risk performance.
- Drive product settings optimisation (spreads, fees, promotions); build client segmentation, LTV, and deposit-conversion models; run controlled commercial experiments with measurable P&L impact.
- Oversee quoting quality, inventory efficiency, and capital deployment; develop flow-quality scoring for institutional counterparties and liquidity partners.
- Own real-time exposure and P&L attribution monitoring; build and govern A/B-book routing logic, flow-toxicity classification, and hedging strategy quantification.
- Lead the analytics layer for bonus abuse, arbitrage abuse, multi-accounting, and payment fraud detection; collaborate with the fraud operations team to reduce losses and cut detection latency.
- Translate analysis into board-ready recommendations; maintain model governance and analytical independence; lead Stage 2 expansion into additional asset classes.

Requirements:
- 10+ years in quantitative analytics, trading risk, or quant research, with a clear leadership track record.
- Prior ownership of an analytics or risk function—models that changed decisions, not just dashboards.
- Deep domain expertise in at least one of: retail FX/multi-asset brokerage, market making/HFT, or derivatives risk.
- Hands-on applied statistics/ML and data engineering: Python, SQL, large real-time datasets.
- Demonstrated ability to influence commercial and risk decisions at desk-head and executive level.
- Comfortable operating alongside directional market risk.
- Proficient in English and Chinese.