QuantAI - Agentic Quant Manager

Role overview

What you will work on

• Build and evaluate forecasting, pricing, optimization, and decision models, choosing methods that meet evidence-based evaluation requirements.
• Develop forecasting stacks, leakage-safe backtests, probabilistic metrics, and agent-driven model-improvement loops.
• Turn forecasts into commercial decisions and connect optimization tools to trader workflows.
• Evaluate text-heavy decision models using classifiers and language models, with calibration, human review, and held-out testing.
• Write reproducible, tested quant code and package it as MCP tools or reusable agent skills.
• Lead evaluation gates and agent-assisted code reviews; check research for leakage, baseline parity, units, calendars, and out-of-sample performance.
• Lead client project teams, go-to-market work, demonstrations, proposals, and presentations; coach teammates and set review standards.

Must-have qualifications

• Bachelor's degree in a quantitative field or related field; associate degree accepted with two additional years of relevant experience and a strong applied record.
• At least five years in a quantitative role where models informed trading, hedging, pricing, or risk decisions.
• Hands-on experience in at least one listed specialty: derivatives pricing, curve or volatility modeling, forecasting, optimization, or systematic strategy research.
• Rigorous backtesting against explicit baselines, leakage control, and honest reporting of results.
• Hands-on mathematical optimization experience tied to commercial or operational decisions.
• Expert Python and quantitative computing, plus Git collaboration, tests, and code review.
• Daily coding-agent practice and experience building or extending an agent workflow, tool, or skill used by others.
• Experience adapting or fine-tuning modern models for quantitative problems and testing gains out of sample against a classical baseline.
• Technical leadership, rigorous review, and clear communication of quantitative work and its limitations.

Nice-to-have qualifications

• Commodity or energy trading experience, including physical market constraints and conventions.
• Advanced quantitative degree; foundation-model training or fine-tuning; scenario, geospatial, weather, or climate modeling.
• MCP servers, agent skills, evaluation harnesses, agent frameworks, model calibration, or production optimization experience.
• Agent-driven model-improvement loops, accelerator compute, AWS or Azure, or client-facing consulting and business development experience.

Team and environment

• Work alongside commodity experts, quants, full-stack engineers, and analysts in a small global team.
• Build with shared agent workflows, research and verification skills, curated knowledge bases, and review-and-repair harnesses.
• Help determine which trading demonstrators are ready to become repeatable client offerings based on evidence.
• Travel to client sites when project work requires it.