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.
• Drive the ingestion and embedding of key data sources: broker research (email and platform), central bank transcripts, internal research notes, and PM communications.
• Ensure data quality, naming conventions, and governance standards for all AI-accessible datasets.
• Work with Technology to build and maintain data pipelines that keep AI tools fed with current, relevant information.

Platform Liaison & Priority Advocacy

• 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.
• Participate in cross-strategy AI working groups, share DMFI use cases, and import best practices from other strategy sets.
• Translate business requirements into technical specifications that the AI engineering team can deliver.
• Stay current on the rapidly evolving AI landscape (new models, tools, capabilities) and assess relevance for DMFI.

Compliance & Governance

• Ensure all AI-derived analytics and outputs have appropriate audit trails for compliance purposes.
• Work with Compliance to establish guardrails for AI usage in trading contexts.
• Maintain documentation of all active AI tools, datasets, and workflows.

What You’ll Bring

• 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).
• Genuine, demonstrable passion for AI — personal projects, open-source contributions, research papers, or equivalent evidence of self-directed learning.
• Hands-on proficiency with Python; experience with ML frameworks (PyTorch, TensorFlow, HuggingFace), LLM APIs (OpenAI, Anthropic), and data manipulation libraries (pandas, numpy).
• Familiarity with NLP concepts: embeddings, vector databases, RAG architectures, prompt engineering…