Overview
Quantitative Developer to join the Fundamental Equity COO team, embedded within the business to build tools, dashboards, and data pipelines that improve productivity, deepen AI adoption, and make better use of data access. AI integration is a core part of this role, including LLMs, agentic workflows, and AI-powered tooling in the investment process. You should bring hands-on experience and a clear view of where these technologies are heading. The role is close to the investment process, requiring proactive, delivery-focused work that yields high-quality outputs and immediate value for investment professionals.
What You’ll Do
- Write clean, well-structured, maintainable code and contribute to engineering practices within the team, including CI/CD pipelines and version control, with a contribution to firmwide best practices for broad reuse
- Build, deploy, and maintain internal tools and dashboards that surface quantitative outputs, portfolio analytics, and market data in clean, intuitive interfaces used by investment professionals
- Contribute to the AI integration layer with central Technology teams: implement and operate AI capabilities tailored to Fundamental Equity, spanning LLM APIs, MCP servers, and agentic workflows, with continuous reassessment of best approaches
- Work closely with quant researchers to turn analytical outputs from prototype into reliable, production-ready applications
- Continuously identify where AI tooling can reduce friction, improve output quality, or unlock capabilities not currently available
What You’ll Bring
- 2–5 years of relevant experience: quantitative development, dev strats, analytics engineering, product management, or quantitative analysis at a bank, hedge fund, asset manager, or financial data provider
- Strong Python proficiency; solid SQL and database usage skills (relational and/or time-series)
- Demonstrated experience building dashboards and UIs actively used by investment teams or professional users in production
- Hands-on experience with AI/LLM integration: API usage, prompt engineering, tool use, and retrieval-augmented workflows
- Practical familiarity with MCP servers, agentic frameworks, or multi-model orchestration architectures
- Experience with AI-assisted development tools as part of daily engineering workflow
- Sound software engineering fundamentals: version control, structured codebases, CI/CD pipelines, documentation, testing
- Active engagement with the AI development landscape to test new tools and translate insights into practical building decisions
- Collaborative by nature; able to work across technical and non-technical audiences to share ideas and contribute to a positive team culture
- Product-minded: focus on user needs and the problem being solved, not just code execution
- Comfortable in a business-facing team with evolving requirements and a fast pace; not a research-lab environment
- Self-starter who owns projects end-to-end, from scoping to maintenance and iteration
- Intellectually curious about financial markets and the investment process
We’d Love If You Had
- Exposure to alternative data ingestion or vendor API integrations
- Prior experience in a hedge fund or multi-manager platform
- Comfortable working within AWS: EC2, containerized workloads, and infrastructure managed via Terraform or similar IaC tooling
- Experience with Docker, Kubernetes, and workflow orchestration frameworks (Prefect, Airflow, or equivalent)
Who We Are
Schonfeld is a global multi-manager hedge fund that leverages internal and external portfolio manager teams to invest across four main strategies: Quant, Tactical, Fundamental Equity and Discretionary Macro & Fixed Income. We invest in proprietary technology, infrastructure and risk analytics to seek risk-adjusted returns for our investors.
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