Website:
amunra.io
Job details:
The Role
We are looking for a PhD Quantitative Researcher to join Amunra’s research team.
The role is suited to a researcher with a background in:
- market ecology;
- econophysics;
- complexity science;
- statistical physics;
- applied mathematics;
- nonlinear dynamics;
- network science;
- computational science;
- quantitative finance;
- econometrics;
- machine learning;
- or a related quantitative discipline.
You will work on empirical research problems involving financial-market behaviour, risk, market structure and changing market regimes.
This is not a conventional quantitative role centred solely on fitting predictive models or optimising backtests. We are looking for someone interested in understanding the mechanisms through which market behaviour changes and in developing research that is robust, interpretable and practically useful.
Responsibilities
The researcher will be expected to:
- develop and test quantitative hypotheses using financial-market data;
- study market behaviour, risk formation and regime changes;
- apply methods from complexity science, statistical physics and quantitative finance;
- analyse large, noisy and potentially high-frequency datasets;
- develop statistical and computational models for market research;
- design reproducible empirical experiments;
- evaluate model stability across different market conditions;
- distinguish genuine relationships from overfitting and data-mined results;
- translate theoretical concepts into testable research questions;
- document research methods, results and limitations clearly;
- communicate findings to both technical and non-technical stakeholders;
- collaborate with quantitative researchers, data engineers and investment professionals;
- contribute to the development of internal research tools and analytical systems.
Areas of Research
Depending on the candidate’s background and interests, research may involve:
- financial-market complexity;
- market microstructure;
- volatility and liquidity;
- regime identification;
- nonlinear time-series analysis;
- critical transitions and structural breaks;
- market resilience and instability;
- interaction and feedback effects;
- network analysis;
- information theory;
- agent-based or ecological approaches to markets;
- statistical learning applied to financial systems.
Candidates are not expected to have experience in every area.
Required Qualifications
- PhD in a relevant quantitative discipline.
- Strong foundation in probability, statistics and time-series analysis.
- Experience conducting rigorous empirical or computational research.
- Proficiency in Python and scientific computing tools.
- Ability to work with complex, noisy and large datasets.
- Understanding of experimental design, model validation and overfitting.
- Ability to formulate clear and falsifiable research questions.
- Strong written and verbal communication skills.
- Intellectual independence and a willingness to challenge assumptions.
- A genuine and demonstrable interest in financial markets.
- Prior financial-industry experience is helpful but not mandatory
Click on Apply to know more.