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Quantitative Researcher - Machine Learning

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AKUNA CAPITAL

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Location:
Australia , Sydney

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Contract Type:
Not provided

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Salary:

Not provided

Job Description:

Akuna’s Quantitative Trading and Research team is looking to add experienced Quant Researcher specialized in Machine Learning to a team of mathematicians, statisticians and technologists. This team creates trading strategies scientifically by combining its quantitative expertise with sophisticated understanding of derivatives and financial markets.

Job Responsibility:

  • Develop trading strategies using statistical and machine learning algorithms
  • Design, conduct, and analyse experiments for a deep understanding of derivatives and financial markets
  • Build metrics to evaluate strategy execution and perform post trade analysis
  • Design and implement optimization algorithms for portfolio construction
  • Develop quantitative models describing market behavior
  • Advance existing initiatives and explore opportunities for new research topics

Requirements:

  • 3+ years' strong professional work experience in statistics, machine learning, option trading or related area
  • Hands on experience using a modern ML tool in a trading application
  • Bachelors, Masters or PhD in a technical field –Statistics, Computer Science, Mathematics, Physics, Engineering (or a related subject), must be completed upon employment
  • Proven research background in academic or professional environment
  • Experience building mathematical models for complex real-world problems
  • Expertise in Math and statistics
  • Strong Python programming skills
  • Machine Learning/Data Science experience with modern and classic ML tools such as Neural Networks, Tree models, and Ensemble Learning, applied to large and complex data sets in a trading application.

Nice to have:

  • Collaboration, teamwork, and excellent communication skills
  • Strong drive to deliver meaningful results
  • Financial experience or knowledge of options trading is a plus

Additional Information:

Job Posted:
December 05, 2025

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