Front Office Subject Matter Expert
Pubblicato il 20-10-2026 - Behavox in Bologna
Behavox is shaping the future for how businesses harness their most important raw material - data. Our mission is bold: Organize enterprise data into actionable information that protects and promotes the business growth of multinational companies around the world.
From managing enterprise risk and compliance to maximizing revenue and value, our data operating platform presents a widespread opportunity to build multilingual, AI/ML-based solutions that activate data for every function within a global enterprise.
Our approach is unique, and it’s validated by our customers who tell us to keep forging ahead because no one else is aggregating, analyzing, and acting on data to uncover opportunities or solve problems quite the way we are.
We are looking for fearless innovators who have an insatiable appetite for building what no one has built before.
About the Role
The Front Office Subject Matter Expert is an experienced individual contributor in the Subject Matter Expert (SME) team.
The role is the primary owner of one or two related asset classes and makes sure that the Behavox Financial Data Model (BFDM) and the Unified Risk Taxonomy (URT) reflect how trading desks, venues and regulation actually work. The URT is the common language for risks across preventive controls (such as conflicts of interest and conduct) and detective controls (such as trade and communications surveillance).
Working with little day-to-day instruction, the role defines asset class data specifications, turns market abuse typologies into scenarios for Polaris, the Behavox trade surveillance product, and advises product, engineering and data science teams. It supports the revenue team in client meetings, demos and requests for proposal (RFPs) alongside a senior SME or the manager, but does not lead engagements with high-profile prospects or regulators on its own.
The role is AI-native: it works with AI agents and agentic workflows, not only chat assistants, specifying requirements for agent-based pip