وصف الوظيفة
Role Overview
Senior Quantitative Analyst - Trading Risk & Fraud at Zeal Group, London.
Company Overview
Zeal Group is an award-winning global FinTech powerhouse with 700+ professionals across London, Europe, Asia, MENA, and South America. Through subsidiary Traze, the company delivers advanced multi-asset trading solutions across Gold, Oil, FX, Indices, and Cryptocurrencies for fast-moving, high-volatility markets.
Role Purpose
Own the technical and analytical backbone of the trading risk and fraud function. Design, build, and maintain systems that detect toxic flow, latency arbitrage, and abusive trading behavior, then use that technology to investigate, classify, and act on risk. This is a hands-on, individual-contributor role reporting to the Head of Risk with high autonomy and minimal oversight.
Key Responsibilities
Detection & Infrastructure
- Design and build backend infrastructure, monitoring tools, and detection algorithms to identify toxic flow, latency arbitrage, and abusive trading patterns.
- Own end-to-end delivery: from raw data extraction and hypothesis testing through to production deployment of monitoring and alerting systems.
Investigation & Analysis
- Use systems you build to investigate trading and non-trading activity, detect suspicious behavior, and report findings.
- Analyze existing and new trading products and promotions for fraud and abuse exposure and propose mitigations.
- Classify clients to support A-book/B-book decisioning and profitable execution outcomes.
Process Optimization
- Continuously identify gaps in current processes and design new logic to increase automation and reduce manual intervention.
Mentoring
- Mentor others on technical and analytical approaches as the function grows.
Qualifications & Experience
- 3+ years' experience in Quant/Trading role or FX/CFD brokerage environment in a quantitative, risk, or analytics function.
- Bachelor's degree or higher in Mathematics, Statistics, Physics, Finance, or a related quantitative field.
- Demonstrated experience independently building and owning production-grade tools, dashboards, or systems (not just using existing ones).
- Evidence of data science-related projects, ideally end-to-end driven projects.
- Strong knowledge of financial trading products, trading conditions, and execution mechanics.
- Understanding of fraud and abuse patterns in trading: toxic flow, latency arbitrage, manipulation.
Skills & Competencies
- Proficient experience with Python (mandatory).
- Experience with SQL (preferred).
- Strong foundation with machine learning and data science in financial markets.
- Experience or understanding of A/B-book risk models, transaction cost analysis, or execution analytics (preferred).
- Exposure to regulatory reporting: MAR, FCA, CySEC, ASIC (preferred).
- Comfortable working with high ownership and low supervision; proactive and self-directed.
- Track record of mentoring junior analysts (preferred).