Quantitative Equity Analyst

Quantitative Equity Developer / Python Engineer (Contract, Remote, 1-Week Sprints) Australian Market-Neutral Systematic Equities Strategies

We 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 Mission

Deliver 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 Scope

• Review 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 & Development

• Enhance 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 Modelling

• Review and improve multi-factor risk models and liquidity-aware transaction-cost functions.
• Automate model calibration using Bayesian optimisation or reinforcement-learning techniques.

Automation & Engineering

• Deliver 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 Profile

• 3+ 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 Framework

• Weekly 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 Milestones

• Week 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 Join

• AI-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