AI Strategy Analyst

Schonfeld·Londonmid

In short

→Analista de Estrategia de IA que implementa herramientas de IA en procesos de inversión macro y renta fija.
→Trabaja con datos, prompts, pipelines y entrenamiento para mejorar la productividad de gestores y analistas.
→Destaca por ser el puente entre inversiones y tecnología, con foco en adopción real y métricas de impacto.

Fluent English required for collaboration with global teams and documentation.

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What they ask for

✓3-5 años tras graduarse, con PhD o fuerte formación técnica (CS, Ciencia de Datos, Finanzas Computacionales, etc.)
✓Interés genuino y base curiosa en IA/ML
✓Capacidad para trabajar con datasets, prompts y workflows de IA
✓Habilidades de comunicación y formación para PMs y analistas
✓Experiencia en gestión de datos y pipelines (ingestión, embeddings)
✓Capacidad de traducir necesidades de negocio en especificaciones técnicas

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

SchonAIClaudeBloombergCiti VelocityDTCCVectorised document storesResearch embeddingsEmail ingestion pipelinesProprietary pod-level dataInternal analytics

Who should you write to at Schonfeld?

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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 t

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