Build statistical models. Challenge market pricing. Solve problems most investors never see.
We're partnering with a highly sophisticated quantitative investment firm that has built one of Australia's most advanced research environments.
The team develops large-scale statistical models that price thousands of financial instruments, identify pricing inefficiencies and transform quantitative research into production-grade systems used by investment professionals every day. Holding periods are typically 2–8 days, with regular intraday trading.
You'll work alongside mathematicians, statisticians, software engineers and quantitative researchers in an environment where intellectual curiosity and technical excellence are genuinely valued.
This is an opportunity for someone who enjoys solving difficult mathematical problems and wants their work to have an immediate real-world impact.
What you'll be doing
- Build statistical and machine learning models for complex financial datasets
- Develop and back-test predictive models
- Work with large,
messy datasets from multiple market sources
- Turn research ideas into production-quality code
- Improve the performance and robustness of existing models
- Work alongside researchers, engineers and investment professionals
What we're looking for
- A degree in Mathematics, Statistics, Actuarial Studies, Computer Science, Physics, Engineering or Econometrics
- 1-3 years’ experience post-Bachelor’s
- Exceptional academic results: 95+ ATAR and a High Distinction average
- Strong Python and/or R, plus SQL
- Experience with large datasets and excellent statistical modelling ability
- Financial markets experience isn’t essential. Strong quantitative thinking is.
Why consider it
- Small team of exceptionally capable people on genuinely difficult problems
- Your models won’t sit on a shelf, as they influence investment decisions and are tested against real markets
- Competitive base and a solid performance bonus
📌 Data Scientist (Funds Management - Engineering Alpha) (Sydney)
🏢 Capital Executive Search
📍 Sydney
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