AI Strategy Analyst

The Role

We are looking for a technically-minded individual with a deep personal interest in AI/ML to join the DMFI COO Office as a dedicated AI Strategy Analyst. This is not a traditional quant or engineering role — it sits at the intersection of investment workflows, data strategy, and applied AI, with a mandate to drive real adoption and measurable impact across our Macro & Fixed Income platform.

We need someone who can get hands-on with training, datasets, prompt engineering, and implementation, while continuing to advocate for DMFI priorities with the platform AI team.

The ideal candidate is 3-5 years out of university, likely with a PhD or strong technical background (computer science, data science, computational finance, physics, engineering, or similar), who has a genuine base-case curiosity about AI and can grow into a leadership position as the function scales. We value intellectual horsepower and hunger over years of experience.

What You'll Do

AI Implementation & Hands-On Delivery

• Own the end-to-end implementation of AI tools and workflows for DMFI PMs and analysts — from scoping use cases through to production deployment and adoption tracking.
• Build, test, and refine custom prompts, skill libraries, and automated workflows tailored to macro/fixed income investment processes.
• Develop and maintain custom datasources (vectorised document stores, research embeddings, email ingestion pipelines) that PMs can query via SchonAI/Claude.
• Work with proprietary pod-level data, market data (Bloomberg, Citi Velocity, DTCC), and internal analytics to create AI-accessible datasets.
• Prototype and iterate on use cases: AI-driven research briefs, trade write-ups, behavioural bias detection, position analytics, and idea generation tools.

Training & PM Adoption

• Design and deliver training programmes for PMs and analysts — from prompt engineering fundamentals to advanced Claude Code sessions.
• Create playbooks, best-practice guides, and reusable templates that lower the barrier to AI adoption.
• Run regular "AI Lab" sessions, demo new capabilities, and build institutional knowledge across the platform.
• Track adoption metrics (usage rates, token spend, hours saved, model adoption) and report on ROI to senior management.
• Identify and address friction points — token budgets, workflow gaps, awareness issues — to drive consistent adoption.

Data Strategy & Dataset Management

• Map and catalogue DMFI's data landscape: what data exists, where it lives, and how to make it AI-accessible.