Quantitative Equity Analyst

Quantitative Equity Developer / Python Engineer (Contract, Remote, 1-Week Sprints)Australian Market-Neutral Systematic Equities StrategiesWe are building market-neutral equity strategies and are seeking a Quantitative Equity Developer who combines quantitative theory, software engineering, and AI-native tooling.This is a contract, remote role (flexible hours) working directly with the fund’s founders to implement and scale systematic strategies grounded in the Grinold & Kahn information-ratio framework.Your MissionDeliver measurable outcomes every week — tested, documented, deployable code — not research slides.Work in one-week sprints where AI tools accelerate every step: research, coding, optimisation, and deployment.Initial ScopeReview and optimise the current research pipeline (Earnings Momentum + Dividend Arbitrage).Automate alpha → risk → cost → execution using FactSet data, Interactive Brokers API (ib_insync), and internal models.Convert a MATLAB dividend strategy to Python, refactor for production, and deploy end-to-end.Develop a daily reversal strategy that systematically trades into the close on the ASX.Core ResponsibilitiesQuant Research & DevelopmentEnhance alpha models with AI-assisted feature discovery and testing (e.g. earnings revisions, behavioural factors).Integrate AI-driven data analysis (e.g. LLMs for unstructured text from broker notes or FactSet transcripts).Risk & Transaction Cost ModellingReview and improve multi-factor risk models and liquidity-aware transaction-cost functions.Automate model calibration using Bayesian optimisation or reinforcement-learning techniques.Automation & EngineeringDeliver weekly sprint outputs through GitHub + Copilot + CI/CD (Docker, GitHub Actions).Implement resilient data and execution pipelines with monitoring and alerting.Use modern AI tools (ChatGPT Pro, Claude, Copilot, AutoGPT) to enhance speed and reliability.Ideal Profile3+ years in a systematic long/short equity fund or quant research environment.Expert-level Python (pandas, NumPy, scikit-learn, cvxpy, API integration).Working knowledge of Grinold & Kahn (information coefficient, breadth, risk budgeting).Experience with FactSet data and IBKR API.Comfort operating in AI-first workflows — prompt-driven coding, automated documentation, continuous model validation.Bonus: MATLAB, ASX microstructure, reinforcement learning, vector databases.Sprint FrameworkWeekly Deliverables: Each sprint ends with production-ready code, tests, and documentation.Feedback Loop: Daily async updates, rapid iteration with founders.Tool Stack: Python, Docker, GitHub, ChatGPT Pro, Copilot, FactSet API, ib_insync.Success MilestonesWeek 1: Full review of research pipeline and first automated data process live.Week 4: End-to-end alpha → risk → execution pipeline fully automated.Month 2+: Continuous release of new alpha modules and risk-model improvements, verified in live trades.Why JoinAI-first culture: leverage LLMs and automation in every workflow.Direct collaboration with portfolio managers; short feedback cycles, visible impact.Remote, flexible, outcome-driven.Opportunity to shape the research stack powering next-generation Australian market-neutral strategies.Quick Fit Checklist Expert Python quant developer Experience with FactSet + IBKR APIs Grinold & Kahn familiarity AI-first mindset (Copilot / ChatGPT / LLM tools) Comfortable delivering production code every week