AI Strategy Analyst

About this role

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…