Quantitative Lead – Client Profiling & Trading Intelligence

Boundless; Dubai; Vor Ort; AED 600.000 - 1.200.000; Vollzeit

Zusammenfassung

Boundless in Dubai seeks a senior quantitative researcher to lead analytics of client trading behavior, execution outcomes, and brokerage exposure. You will define profiling strategies and oversee AI-driven research priorities, while ensuring clear governance and explainable results.

You will develop interpretable models, dashboards and decision-support reports for Dealing, Risk and senior management, translating insights into actionable recommendations.

Qualifikationen

• 8+ years in quantitative research, trading analytics, electronic trading or market risk, including 3+ years leading research initiatives or quantitative staff.
• Direct FX/CFD brokerage experience with a strong understanding of retail client flow, broker execution models, hedging, liquidity and trading risk.
• Strong hands-on Python and SQL, statistical modelling and applied machine-learning skills; ability to review models and investigate results independently.
• Experience building behavioural profiles or trading intelligence using transaction-level data and translating findings into operational decision support.
• Deep understanding of validation pitfalls, including leakage, selection bias, regime changes and misleading results from limited trading histories.
• Ability to explain technical findings clearly to Dealing, Risk and executive management and challenge commercially attractive but weakly supported conclusions.
• Degree in a quantitative discipline; a master's degree or PhD is advantageous but does not replace relevant brokerage experience.

Aufgaben

• Define a client and flow profiling framework using trading frequency, holding periods, instrument mix, position sizing, leverage, timing and execution behaviour.
• Develop AI-led segmentation, clustering, anomaly detection and predictive research priorities, with interpretable statistical baselines and clearly defined business questions.
• Distinguish observed trading behaviour, economic exposure and operational anomalies; avoid treating short-term profitability or a model-generated label as proof of abusive activity.
• Lead the Quantitative AI/ML Engineer's research backlog, review feature and label definitions and establish standards for experiments, documentation and reproducibility.
• Analyse flow characteristics, mark-outs, slippage, concentration and correlated activity to understand execution quality, adverse selection and changes in market conditions.
• Assess broker exposure and hedging outcomes under the approved execution model, separating client trading outcomes, broker revenue, hedge costs and residual market risk.
• Develop dashboards, cohort analyses and decision-support reports that explain the drivers of changes in client behaviour and trading risk.
• Present evidence-based recommendations to Dealing and Risk, including confidence, alternatives and potential client impacts; route implementation through approved decision processes.
• Define monitoring thresholds, review schedules and escalation triggers for profiles and trading intelligence models; ensure profiles can change as new evidence emerges.
• Partner with Compliance on permitted data use and client treatment, and with independent reviewers on material model challenge and validation.
• Coach quantitative staff, maintain a research roadmap and communicate findings to senior leadership without overstating model certainty or implying guaranteed trading performance.

Kenntnisse

Python

SQL

Statistical modelling

Applied ML

Leadership

Ausbildung

Bachelor's or higher in quantitative discipline

Master's degree or PhD advantageous

Tools

MT4/MT5 datasets

Graph analysis

Jobbeschreibung

Lead the use of quantitative analysis and AI to understand client trading behaviour, execution outcomes and the brokerage's exposure to different types of flow. Define a robust client profiling and trading intelligence methodology and turn analytical findings into explainable recommendations for Dealing, Risk and senior management. Combine market knowledge with hands-on research and leadership of quantitative model development.

Key responsibilities

Profiling strategy and research leadership

• Define a client and flow profiling framework using trading frequency, holding periods, instrument mix, position sizing, leverage, timing and execution behaviour.
• Develop AI-led segmentation, clustering, anomaly detection and predictive research priorities, with interpretable statistical baselines and clearly defined business questions.
• Distinguish observed trading behaviour, economic exposure and operational anomalies; avoid treating short-term profitability or a model-generated label as proof of abusive activity.
• Lead the Quantitative AI/ML Engineer's research backlog, review feature and label definitions and establish standards for experiments, documentation and reproducibility.

Trading intelligence and business application

• Analyse flow characteristics, mark-outs, slippage, concentration and correlated activity to understand execution quality, adverse selection and changes in market conditions.
• Assess broker exposure and hedging outcomes under the approved execution model, separating client trading outcomes, broker revenue, hedge costs and residual market risk.
• Develop dashboards, cohort analyses and decision-support reports that explain the drivers of changes in client behaviour and trading risk.
• Present evidence-based recommendations to Dealing and Risk, including confidence, alternatives and potential client impacts; route implementation through approved decision processes.

Model governance and team leadership

• Review out-of-time performance, label quality, regime sensitivity, fairness where relevant and the economic significance of model results before recommending operational use.
• Define monitoring thresholds, review schedules and escalation triggers for profiles and trading intelligence models; ensure profiles can change as new evidence emerges.
• Partner with Compliance on permitted data use and client treatment, and with independent reviewers on material model challenge and validation.
• Coach quantitative staff, maintain a research roadmap and communicate findings to senior leadership without overstating model certainty or implying guaranteed trading performance.

Essential experience and qualifications

• 8+ years in quantitative research, trading analytics, electronic trading or market risk, including 3+ years leading research initiatives or quantitative staff.
• Direct FX/CFD brokerage experience with a strong understanding of retail client flow, broker execution models, hedging, liquidity and trading risk.
• Strong hands-on Python and SQL, statistical modelling and applied machine-learning skills; ability to review models and investigate results independently.
• Experience building behavioural profiles or trading intelligence using transaction-level data and translating findings into operational decision support.
• Deep understanding of validation pitfalls, including leakage, selection bias, regime changes and misleading results from limited trading histories.
• Ability to explain technical findings clearly to Dealing, Risk and executive management and challenge commercially attractive but weakly supported conclusions.
• Degree in a quantitative discipline; a master's degree or PhD is advantageous but does not replace relevant brokerage experience.

Preferred experience

• Experience establishing a quantitative research function or moving profiling models from research into monitored production.
• Experience with MT4/MT5 datasets, liquidity-provider analytics, execution mark-outs and broker risk-book analysis.
• Experience with graph analysis, behavioural clustering, explainable ML and controlled challenger assessments.