Quantitative Researcher (Honours/Masters) — Signal Backtesting

Susquehanna International; Sydney; On-site; AUD 120,000 - 180,000; Full time

Benefits offered by this job

Education program

Casual, non-hierarchical culture

Competitive remuneration and bonus

Private healthcare

Gym allowances

Fully stocked kitchen

Social events

Visa sponsorship

Relocation package

Job summary

Susquehanna International Group – Sydney NSW seeks a Quantitative Researcher for Honours and Masters level candidates to grow in quantitative finance. You will immerse yourself in market data, apply math, statistics, and programming, and backtest trading signals with technologists to productionize them.

Successful candidates demonstrate strong problem-solving, collaboration, and practical computing skills, with no strict requirement for prior finance experience.

Qualifications

• Honours or Master's degree in a quantitative field such as Mathematics, Physics, Statistics, or Economics.
• Exceptional mathematical problem-solving skills.
• Strong communication in a fast-paced and/or highly collaborative environment are essential.

Responsibilities

• Investigate ideas by analyzing market data using mathematics, statistics and programming.
• Backtest research results and collaborate with technologists to productionize trading signals.
• Iterate on strategies to optimize performance as they evolve.
• Engage with researchers, strategists, and traders to gather feedback and brainstorm ideas.
• Drive research cycles from concept to deployment and ongoing refinement.

Skills

Mathematical problem solving

Communication skills

Computing skills

Research experience

Education

Honours or Master's degree in a quantitative field

Job description

Susquehanna International Group – Sydney NSW seeks a Quantitative Researcher for Honours and Masters level candidates to grow in quantitative finance. You will immerse yourself in market data, apply math, statistics, and programming, and backtest trading signals with technologists to productionize them.

Successful candidates demonstrate strong problem-solving, collaboration, and practical computing skills, with no strict requirement for prior finance experience.