AI Strategy Analyst

<p><span style="text-decoration: underline;"><strong>The Role</strong></span></p> <p>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 &amp; Fixed Income platform.<br><br>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.<br><br>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.</p> <p><span style="text-decoration: underline;"><strong>What You'll Do</strong></span></p> <p>AI Implementation &amp; Hands-On Delivery</p> <ul> <li>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.</li> <li>Build, test, and refine custom prompts, skill libraries, and automated workflows tailored to macro/fixed income investment processes.</li> <li>Develop and maintain custom datasources (vectorised document stores, research embeddings, email ingestion pipelines) that PMs can query via SchonAI/Claude.</li> <li>Work with proprietary pod-level data, market data (Bloomberg, Citi Velocity, DTCC), and internal analytics to create AI-accessible datasets.</li> <li>Prototype and iterate on use cases: AI-driven research briefs, trade write-ups, behavioural bias detection, position analytics, and idea generation tools.</li> </ul> <p>Training &amp; PM Adoption</p> <ul> <li>Design and deliver training programmes for PMs and analysts — from prompt engineering fundamentals to advanced Claude Code sessions.</li> <li>Create playbooks, best-practice guides, and reusable templates that lower the barrier to AI adoption.</li> <li>Run regular "AI Lab" sessions, demo new capabilities, and build institutional knowledge across the platform.</li> <li>Track adoption metrics (usage rates, token spend, hours saved, model adoption) and report on ROI to senior management.</li> <li>Identify and address friction points — token budgets, workflow gaps, awareness issues — to drive consistent adoption.</li> </ul> <p>