<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 & 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 & 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 & 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>Data Strategy & Dataset Management</p> <ul> <li>Map and catalogue DMFI's data landscape: what data exists, where it lives, and how to make it AI-accessible.</li> <li>Drive the ingestion and embedding of key data sources: broker research (email and platform), central bank transcripts, internal research notes, and PM communications.</li> <li>Ensure data quality, naming conventions, and governance standards for all AI-accessible datasets.</li> <li>Work with Technology to build and maintain data pipelines that keep AI tools fed with current, relevant information.</li> </ul> <p>Platform Liaison & Priority Advocacy</p> <ul> <li>Act as the primary interface between DMFI and the central AI/Technology team — representing PM priorities, advocating for resources, and ensuring DMFI's roadmap items are appropriately prioritized.</li> <li>Participate in cross-strategy AI working groups, share DMFI use cases, and import best practices from other strategy sets.</li> <li>Translate business requirements into technical specifications that the AI engineering team can deliver.</li> <li>Stay current on the rapidly evolving AI landscape (new models, tools, capabilities) and assess relevance for DMFI.</li> </ul> <p>Compliance & Governance</p> <ul> <li>Ensure all AI-derived analytics and outputs have appropriate audit trails for compliance purposes.</li> <li>Work with Compliance to establish guardrails for AI usage in trading contexts.</li> <li>Maintain documentation of all active AI tools, datasets, and workflows.</li> </ul> <p><span style="text-decoration: underline;"><strong>What You'll Bring</strong></span></p> <ul> <li>3-5 years post-university; PhD or Master's in a quantitative/technical discipline strongly preferred (Computer Science, Data Science, Machine Learning, Computational Finance, Physics, Mathematics, Engineering, or similar).</li> <li>Genuine, demonstrable passion for AI — personal projects, open-source contributions, research papers, or equivalent evidence of self-directed learning.</li> <li>Hands-on proficiency with Python; experience with ML frameworks (PyTorch, TensorFlow, HuggingFace), LLM APIs (OpenAI, Anthropic), and data manipulation libraries (pandas, numpy).</li> <li>Familiarity with NLP concepts: embeddings, vector databases, RAG architectures, prompt engineering, fine-tuning.</li> <li>Comfort working with large datasets and building data pipelines (SQL, cloud storage, APIs).</li> <li>Interest in or exposure to financial markets — particularly macro/fixed income — is a strong plus but not required; we will teach the domain to the right technical candidate.</li> <li>Excellent communication skills — ability to explain complex technical concepts to non-technical PMs a