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.
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 Responsibilities
Quant 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 up-to-date 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
Seniority level
- Mid-Senior level
Employment type
- Contract
Job function
- Finance and Sales
- Venture Capital and Private Equity Principals
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