Director, Product Manager - Institutional Platform

Director, Product Manager (Portfolio Margin and Risk) Galaxy Digital Inc. is a global leader in digital assets and data center infrastructure, growing the economy that runs on code. Galaxy delivers the onchain infrastructure that connects institutions to digital assets, including trading, advisory, asset management, staking, self-custody, and tokenization. Galaxy also develops and operates data center infrastructure to power AI and HPC workloads. Anchored by its Helios campus in Texas, Galaxy is building a multi-gigawatt pipeline of more than 5.7 GW of potential capacity, positioning it among the largest and fastest-growing data center developers in North America. The Company is headquartered in New York City, with offices across North America, Europe, the Middle East, and Asia. Galaxy is seeking a seasoned Product Manager with 7+ years of experience across derivatives, portfolio risk, and margin infrastructure. This is a high-impact role that sits at the nexus of trading, risk, quantitative modeling, and technology. You will lead the design and evolution of our portfolio margin and risk framework — helping define the capital efficiency, safety, and scalability of Galaxy's multi-asset / multi-instrument trading platform. This role demands a rare blend of quantitative fluency, systems architecture understanding, and trading intuition. You should deeply understand how proper portfolio margin constructs work, how risk-based margin is calculated on a portfolio basis, and how linear and derivative instruments interact to drive capital requirements. You must understand what "good" looks like for a franchise trading firm — from both a trader usability perspective and a balance sheet efficiency perspective. We are an AI-first product organization. You will be expected to leverage AI tools to: Prototype solutions in parallel with requirements writing Research alternative design approaches and model frameworks Generate comparative analyses of margin methodologies Produce stakeholder-ready solution decks and technical documentation Accelerate iteration cycles between trading, risk, and engineering You should be comfortable using AI as a force multiplier — not as a novelty — embedding it directly into your product development workflow. You should have experience building or enhancing margin systems at a bank, exchange, clearinghouse, or fintech trading platform. Ideally, you've worked in a smaller fintech or greenfield environment where you have seen the full lifecycle end-to-end — from risk model design and instrument setup to pre-trade buying power logic and post-trade margin recalculation. If your experience is primarily from a large bank or institution, you must demonstrate that you have operated effectively in ambiguous or grey environments — driving new builds, defining frameworks from scratch, and navigating incomplete specifications without relying on rigid legacy processes. Using tooling including AI, you must be able to create prototypes and detailed product requirements from scratch — grounded in how traders, risk managers, and institutional clients actually use margin and risk systems. These requirements must be specific enough to partner effectively with engineering, quant, and risk stakeholders to deliver scalable, real-time systems but grounded in a wider product context. Strong preference for experience at client-facing, risk-taking trading firms such as options market makers, prime brokers, exchanges, or IB sales & trading desks — especially where you've built or enhanced automated / low-touch risk and margin systems. Portfolio Margin & Risk Framework Design and evolve a portfolio-based margin system spanning spot, futures, options, swaps, and structured derivatives. Translate scenario-based, shock-based, and correlation-based margin methodologies into scalable product requirement and operationalize VaR and Expected Shortfall. Implement portfolio-level offsets, concentration add-ons, liquidity haircuts, and regulatory overlays where applicable. Pre-Trade Buying Power & Margin Impact Architect systems that estimate incremental margin impact before a trade is executed. Design APIs and internal tools that allow traders and clients to understand margin utilization in real time. Define logic for cross-margin eligibility and portfolio-level capital efficiency checks. Instrument & Risk Data Architecture Define canonical instrument representations and ensure correct modeling of multipliers, expiries, funding mechanics, settlement types, and lifecycle events. Design position netting logic across accounts and sub-accounts. Partner with engineering to build real-time risk factor ingestion and portfolio aggregation engines while optimizing data source and normalization to support real-time recalculation. Real-Time Risk & Margin Systems Architect real-time portfolio risk views for traders and risk managers and event-driven recalculation frameworks. Design margin call logic, risk thresholds, and liquidation workflows. Ensure the system behaves predictably and safely under stress scenarios and market dislocations. Centralized Risk & Cross-Product Integration Drive the integration of margin engines with pricing, P&L, booking, and treasury systems. Partner with funding and treasury teams to align margin methodology with balance sheet and capital considerations. Align high-touch OTC and low-touch trading channels under a unified risk framework. End-To-End Workflow Management Architect workflows spanning pre-trade margin estimation, trade execution, booking, portfolio aggregation, margin calculation, and reporting. Integrate across asset classes and trading styles (spot, perps, options, structured products).Ensure a consistent user experience across internal trading desks and institutional clients and across risk systems. Cross-Functional Leadership Own roadmap development and margin methodology evolution. Lead tradeoff discussions across performance, capital efficiency, regulatory constraints, and usability closely with engineering, quant, trading, risk, treasury, compliance, and design teams. Drive delivery timelines and maintain strong feedback loops with front-office users. AI Implementation Embrace and champion the thoughtful adoption of AI to improve team performance and business outcomes. Leverage AI tools (e.g., generative AI, automation platforms, data copilots) to improve productivity, decision-making, and output quality in your day-to-day work.