We are supporting a leading global investment platform in hiring a highly skilled Quantitative Researcher.
This role will focus on building and enhancing a marketmicrostructuredriven research framework for the systematic trading of global equity strategies. The ideal candidate will combine strong statistical and programming expertise with experience handling large financial datasets in a fastpaced, researchdriven trading environment.
Key Responsibilities
Strategy Research & Development
- Collaborate closely with the Senior Portfolio Manager to design and refine systematic global equities strategies.
- Contribute to idea generation, hypothesis testing, and alpha research.
- Conduct endtoend research including data gathering, cleaning, feature creation, modeling, and backtesting.
Data Engineering & Market Microstructure Research
- Work handson with multiple exchange data sets, ensuring highquality data pipelines through assessing, cleaning, and building features.
- Analyze large, complex datasets using advanced statistical learning techniques and marketmicrostructure methods.
Model Implementation
- Implement scalable, flexible, and efficient dataextraction frameworks using existing tools.
- Optimize Python and C++ code to support highscale systematic research workflows.
- Create new data features and extend existing research infrastructure.
Required Technical Skills
- Strong expertise in Python (data analysis, scientific computing, statistical modeling).
- Proficiency with modern data science tooling:
- pandas, numpy, sklearn, Jupyter.
- Experience with C++ (preferred for feature creation and performancecritical components).
- Strong understanding of:
- Quantitative finance
- Probability theory
- Regression and statistical modelling
- Mathematical foundations behind systematic trading strategies
- Ability to clearly communicate complex research outputs to stakeholders.
Preferred Experience
- 2+ years working in a systematic trading environment, ideally focused on equities.
- Experience handling and transforming multiple vendor or exchange datasets, including assessing, cleaning, and creating predictive features.
- Track record of collaborating with PMs, engineers, and researchers in a fastpaced, iterative environment.
Highly Valued Attributes
- Strong intuition for the predictive power of features and datasets.
- Exceptionally rigorous, detailoriented, and selfdriven approach to research.
- Ability to work independently while contributing to a collaborative research environment.
- Curiosity, intellectual agility, and motivation to grow rapidly.
