Regulatory Quant & Intelligence Specialist (Regulatory Compliance)

Role Overview

Key Responsibilities

Regulatory Decision Modelling

• Translate legislation, regulatory guidance and internal policies into structured decision frameworks, rule sets and scoring models that can be embedded into compliance processes and tooling.
• Define quantitative thresholds (risk scores, confidence levels, escalation triggers) that determine when automated decisions are appropriate versus when human review is required, aligned with regulatory expectations and internal governance.

AI-Enabled Compliance Workflows

• Design and prototype AI-powered workflows for regulatory research, licensing analysis, policy drafting, onboarding/KYC, surveillance and incident management within the Regulatory Compliance function.
• Collaborate with engineers and data teams to implement LLM/RAG/agentic workflows that support compliance advisory, regulatory mapping and ongoing monitoring, ensuring strong grounding, documentation and audit trails.

Quant Analysis and Evaluation

• Build and maintain simple analytical/quantitative models to assess regulatory risk, scenario outcomes and potential enforcement impact, supporting proactive compliance strategy and licence/posture decisions.
• Design evaluation frameworks to periodically test and calibrate decision models and AI-enabled workflows against real-world cases, internal incidents and regulatory feedback.

Regulatory Research and Intelligence

• Conduct structured regulatory research across key jurisdictions (e.g. Singapore, EU/EEA, UK, Australia, Middle East) and maintain internal views on how evolving rules impact existing and new products.
• Convert regulatory developments into machine-readable rules, guidance notes and internal playbooks that can be consumed by compliance staff and AI systems.

Governance, Documentation and Stakeholder Engagement

• Draft and maintain documentation for decision frameworks, model assumptions, limitations and human-in-the-loop controls, aligned with AI governance and compliance best practices.
• Partner with Legal, Product and Engineering to embed regulatory requirements into product design and internal tools, and support responses to regulators, auditors and internal risk committees where AI-enabled decisions are involved.

Requirements (Mid-Level under Compliance)

• Quant mindset: technically fluent regulatory professional who thinks in systems - prompts, retrieval and evaluation - and redesign traditional compliance workflows from first principles into executable decision models used by both AI (LLMs, triage engines, agentic workflows, data pipelines) and humans.
• Instinctively seeks the simplest, most automated way to deliver complex work without compromising quality or auditability.
• 3–5 years' experience in regulatory compliance, financial services law, or a related role within a financial institution, crypto/fintech firm, trading venue or regulator.
• Strong understanding of financial services and VASP regulation, licensing frameworks, and cross-border regulatory issues (e.g. MAS, AUSTRAC, FCA, MiCAR, ADGM or similar regimes).
• Demonstrated ability to independently build structured, tech-supported workflows or internal tools for legal/compliance processes (e.g. using Python, scripting, or no-/low-code platforms).
• Experience working with or alongside LLMs, APIs, workflow orchestration or data/analytics platforms, with comfort iterating on prompts, retrieval strategies and evaluation metrics.
• Strong analytical and computational thinking skills, able to formalise complex regulatory reasoning into clear models, diagrams and structured guidance for both humans and systems.
• Excellent communication skills, with the ability to bridge Legal, Compliance, Engineering and Product, and to explain complex decision logic to non-technical stakeholders and senior management.
• High degree of professional judgement, integrity and ownership, with comfort operating in fast-moving, ambiguous regulatory environments typical of crypto and fintech.

Profile / Behavioural Traits

• Systems thinker with a builder's mindset, naturally inclined to redesign workflows from first principles rather than only optimise existing steps.
• Technically curious and comfortable experimenting with AI tools and emerging technologies within a governed, compliant framework.
• Pragmatic, commercially aware and able to balance regulatory risk, business objectives and operational constraints when designing decision models.