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.