Senior Quantitative Analyst - Trading Risk & Fraud

About the role

Requirements

• Must have
• - 3+ years' experience in Quant/Trading OR FX/CFD brokerage environment, in a quantitative, risk, or analytics role.
• - Proficient experience with Python is mandatory.
• - Demonstrated experience independently building and owning production-grade tools, dashboards, or systems (not just using existing ones).
• - Can provide 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/abuse patterns in trading (toxic flow, latency arbitrage, manipulation).
• - Bachelor's degree or higher in Mathematics, Statistics, Physics, Finance, or a related quantitative field.
• - Comfortable working with high ownership and low supervision; proactive and self-directed.
• Preferred
• - Experience with SQL.
• - Experience or understanding of A/B-book risk models, transaction cost analysis, or execution analytics.
• - Exposure to regulatory reporting (MAR, FCA, CySEC, ASIC).
• - Track record of mentoring junior analysts.
• This role suits someone who wants to build something from the ground up and use it, not just analyze data others have prepared. If you're a quant who thinks in systems, takes initiative without being asked, and wants ownership over a real risk function — this is built for you.

Responsibilities

• - Design and build the backend infrastructure, monitoring tools, and detection algorithms used to identify toxic flow, latency arbitrage, and abusive trading patterns.
• - Use the 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/abuse exposure and propose mitigations.
• - Classify clients to support A-book/B-book decisioning and profitable execution outcomes.
• - Own end-to-end delivery: from raw data extraction and hypothesis testing through to production deployment of monitoring and alerting systems.
• - Continuously identify gaps in current processes and design new logic to increase automation and reduce manual intervention.
• - Mentor others on technical and analytical approaches as the function grows.

Skills

• Python
• Data science
• Machine learning
• Quantitative analytics
• Trading risk
• Fraud detection
• A/B-book risk models
• Transaction cost analysis
• Execution analytics
• SQL
• Production-grade tooling
• Dashboards
• Monitoring tools
• Data extraction
• Hypothesis testing