Schonfeld - AI Strategy Analyst
Requirements
• 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 manager teams around the world, seeking to capitalize on inefficiencies and opportunities within the markets. We draw from decades of experience and a significant investment in proprietary technology, infrastructure and risk analytics to invest across four main strategies: Quant, Tactical, Fundamental Equity and Discretionary Macro & Fixed Income.
Responsibilities
• 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.
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