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, fine-tuning. • Comfort working with large datasets and building data pipelines (SQL, cloud storage, APIs). • 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. • Excellent communication skills — ability to explain complex technical concepts to non-technical PMs and translate vague business needs into concrete technical solutions. • Self-starter mentality: comfortable with ambiguity, able to prioritise independently, and driven to ship tangible outcomes rather than just produce analysis. • Collaborative and low-ego; able to work across seniority levels from junior analysts to senior PMs and C-suite. What Success Looks Like (First 12 Months) • Measurable increase in PM AI adoption rates across DMFI. • At least 3-5 fully deployed, production-quality AI workflows generating demonstrable time savings or insight generation for PMs. • Complete catalogue of DMFI datasets with clear AI-accessibility status and roadmap. • Regular training cadence established with positive PM feedback. • Clear prioritisation framework agreed with central AI team for DMFI-specific enhancements. • Quantified ROI metrics linking AI usage to operational efficiency and/or investment edge. Who we are Schonfeld is a global multi-manager hedge fund that strives to deliver industry-leading risk-adjusted returns for our investors. We leverage both internal and external portfolio m