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Develop quant tools & data visualisations to support Fixed Income portfolio management

Permanent role based in Melbourne or Sydney

Your New Role

This role is designed to develop quantitative tools and data visualisations to support the management of the Fixed Income sector portfolios and Sector Allocation process. This includes data ingestion, storage, manipulation, analysis and visualisation using tools such as SQL/Azure/Python/Power BI.

Key Responsibilities include:

• Leading the transformation of relevant data into different aggregated views of the portfolio and building scalable and maintainable data pipelines;
• Researching, testing and validating quantitative signals used in sector allocation and portfolio construction decisions, including macro, valuation, risk and positioning indicators;
• Designing robust back-testing and monitoring frameworks to assess signal stability and identify failure modes within the core simulation engine;
• Working with Portfolio Managers to translate approved signals into repeatable, production ready inputs to portfolio construction processes;
• Developing portfolio optimization systems that incorporate real-world constraints and evaluate portfolio construction decisions;
• Embedding controls and documentation such that signal usage is transparent, reviewable and compliant with governance expectations;
• Identifying & implementing appropriate uses of machine learning and AI to improve data quality, insight generation, monitoring, and automation within portfolio construction workflows;
• Establishing disciplined cycles of research, experimentation, and integration to institutional-grade systems to deliver systematic and repeatable outcomes;
• Develop new research ideas, particularly on risk analytics, to introduce new approaches to risk evaluation and management;
• Owning & maintaining modular, reusable code libraries for portfolio construction, sector allocation and risk analysis that can be shared across teams;
• Defining & implementing coding standards, version control, testing and documentation practices appropriate for production investment systems.

What You’ll Need

Essential:

• Several years’ experience working within an institutional investment manager;
• Proficiency in SQL/Azure database environments;
• Highly proficient in Python and committed to writing clean, scalable code accompanied by thorough, clear documentation;
• Expertise in Power BI or similar data visualization tools;
• Expertise in data science best practices and the versatility to tailor methodologies to specific project needs and business goals;
• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field;
• Experience in preparation and delivery of reports and/or research findings.

Desired:

• Post-graduate qualification(s) or Chartered Financial Analyst (CFA);
• Familiarity with machine learning algorithms and statistical modelling;
• Experience in data analytics, database management, and systems development;
• Experience in using Aladdin for portfolio construction, risk, attribution etc.

• Leading the transformation of relevant data into different aggregated views of the portfolio and building scalable and maintainable data pipelines;
• Researching, testing and validating quantitative signals used in sector allocation and portfolio construction decisions, including macro, valuation, risk and positioning indicators;
• Designing robust back-testing and monitoring frameworks to assess signal stability and identify failure modes within the core simulation engine;
• Working with Portfolio Managers to translate approved signals into repeatable, production ready inputs to portfolio construction processes;
• Developing portfolio optimization systems that incorporate real-world constraints and evaluate portfolio construction decisions;
• Embedding controls and documentation such that signal usage is transparent, reviewable and compliant with governance expectations;
• Identifying & implementing appropriate uses of machine learning and AI to improve data quality, insight generation, monitoring, and automation within portfolio construction workflows;
• Establishing disciplined cycles of research, experimentation, and integration to institutional-grade systems to deliver systematic and repeatable outcomes;
• Develop new research ideas, particularly on risk analytics, to introduce new approaches to risk evaluation and management;
• Owning & maintaining modular, reusable code libraries for portfolio construction, sector allocation and risk analysis that can be shared across teams;
• Defining & implementing coding standards, version control, testing and documentation practices appropriate for production investment systems.

• SQL/Azure database environments
• Python
• Power BI or similar data visualization tools
• Data science best practices
• Machine learning algorithms
• Statistical modelling
• Data analytics
• Database management
• Systems development
• Aladdin for portfolio construction, risk, attribution etc.

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field
• Post-graduate qualification(s) or Chartered Financial Analyst (CFA)