# AI Strategy Analyst at Schonfeld > United Kingdom · Unspecified · Other · open. Source: https://alderhq.app/jobs/greenhouse%3Aschonfeld%3A8031499 (read 2026-11-06). ## Facts (observed) - Company: [Schonfeld](https://alderhq.app/companies/schonfeld) - Location text: London, England, United Kingdom; London - Where it can be done from (inferred): United Kingdom (evidence: "Location: London, England, United Kingdom; London") - Posted: 2026-06-29 (130 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://job-boards.greenhouse.io/schonfeld/jobs/8031499 ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (London, n 1852): p25 £45k, median £57k, p75 £73k ## Description (the employer's text) 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. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Aschonfeld%3A8031499 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.