Quant Researcher & Trader
High Street Resources Open · verified Oct 24, 2026 At a glance
Mid-level Data Science role at High Street Resources. Dubai · full-time.
Pay not stated Data Science salary
dataroles summary, based on the employer's posting.
What you'll do
- Build predictive signals from market data with statistical and machine-learning methods, then test strategies across equities, futures or derivatives
- Study tick-level and order-book data to assess signal decay, feature stability and sensitivity to changing market regimes
- Create backtesting workflows and check strategies across market conditions for overfitting and generalization
- Assess trading costs, slippage and liquidity, then improve execution rules and inventory models
- Work with traders and engineers to refine strategy ideas and put signals into production systems
What you bring
- You have a master’s or doctoral degree in a quantitative discipline such as mathematics, statistics or computer science
- You bring at least five years in quantitative research or systematic trading
- You can program strongly in Python and have experience handling large-scale market data
- You understand probability, statistics, optimization and time-series modeling
Who this fits
This role suits a mid-to-senior researcher with quantitative training who wants to work across research, strategy validation and execution. It is an onsite, full-time position in Dubai.
From the employer
About the Role
A trading firm is seeking a mid-to-senior Quant Researcher to develop and optimize systematic trading strategies across exchange-traded markets. This role focuses on extracting predictive signals from market data, improving execution logic, and contributing to production-grade algorithmic trading systems in a low-latency environment.
Key Responsibilities
Alpha & Signal Research
- Develop predictive trading signals using statistical modeling and machine learning techniques
- Conduct market microstructure research using tick-level and order-book datasets
- Design and test systematic strategies across equities, futures, or derivatives
- Analyze signal decay, feature stability, and regime sensitivity
Backtesting & Validation
- Build scalable back testing pipelines for strategy evaluation
- Perform robustness testing across multiple market regimes
- Detect overfitting risks and improve model generalization
- Evaluate transaction costs, slippage, and liquidity effects
Execution Optimization
- Improve execution logic and inventory management models
- Support enhancements to quoting strategies in electronic markets
- Collaborate with engineers to deploy production-ready signals
- Optimize latency-sensitive components where required
Cross-Team Collaboration
- Work alongside traders to refine strategy hypotheses
- Partner with engineering teams on implementation workflows
- Contribute to internal research tools and analytics frameworks
Requirements
MSc or PhD in Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or related quantitative discipline
- 5–8+ years experience in quantitative research or systematic trading environments
- Strong programming skills in Python
- Working knowledge of C++ preferred
- Strong foundation in probability, statistics, optimization, and time-series modeling
- Experience working with market data at scale
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