Head of Risk & Decisioning

About the role

Olea, a digital infrastructure platform connecting global liquidity with trade and supply chain opportunities, is looking for a highly analytical, commercially minded and technically fluent leader to establish and lead its Risk & Decisioning function. You will lead the evolution of the decision infrastructure, from traditional quantitative credit risk models to AI-native decision-support systems, and improve the quality, consistency, speed and transparency of decisions across pre-trade, at-trade and post-trade workflows. The role partners with the CRO, Commercial, Legal, Compliance and Operations stakeholders, and works with Product Engineering, Data & AI Operations and other tech teams.

Key responsibilities

• Establish and lead the Risk & Decisioning unit (within Technology) as product owner, turning CRO-defined risk appetite and product requirements into scalable risk controls
• Build a trusted decision layer combining risk expertise, quantitative methods and operating discipline, setting standards for methodology, validation, governance and performance of risk analytics applications
• Own the design, requirements, development and continuous improvement of AI agent decision-support for risk, legal, compliance and commercial areas
• Ensure every model, agent and decision is traceable, explainable and auditable, and define success measures, controls and escalation pathways
• Partner with business stakeholders and direct Product Engineering and Platform teams so decisioning capabilities are implemented, monitored and improved in production
• Mentor risk and engineering colleagues and raise data and AI literacy across the organization

Required profile

• 12-15 years of experience building analytical products, machine learning or AI-enabled capabilities with measurable business outcomes, preferably in financial services, transaction banking, fintech or trade finance
• Domain expertise in credit, counterparty and structural risk across cross-border trade, supply chain finance, invoice financing or factoring is an advantage
• Advanced degree (or first-class bachelor's) in a quantitative discipline such as Data Science, Statistics, Mathematics, Actuarial Science, Finance or Economics
• Experience leading or mentoring small technical or analytical teams

Required skills

• Credit risk models (PD, LGD, EAD), risk analytics and AI-enabled decision-making
• Statistical modelling, machine learning, model governance and AI evaluation, including governing AI agents
• Fraud detection, anomaly monitoring and alternative data is an advantage
• Python, SQL and modern data engineering; TypeScript, FastAPI, Postgres and Docker on cloud-native platforms strongly preferred
• Ability to explain modelling methodology and AI capabilities clearly to non-technical audiences