Quantitative Equity Analyst

vor 4 Wochen

City of Greater Geelong, Österreich Attalis Capital Vollzeit

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 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 Seniority level Mid-Senior level Employment type Contract Job function Finance and Sales Venture Capital and Private Equity Principals #J-18808-Ljbffr

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