Quantitative Researcher

Overview

About Muwazana Muwazana Financial is a technology-first capital-markets firm focused on delivering essential liquidity, and building next-gen trading, research and analytics infrastructure.

The firm combines financial engineering, large-scale data analytics, low-latency networks and algorithmic trading systems to drive market efficiency, transparency and stability. In our Quantitative Research & Data Science team, our quants develop systematic trading signals, build advanced models, leverage large datasets and support real-time market strategies. Role Overview As a Quantitative Researcher at Muwazana, you will be responsible for developing, implementing, validating and refining quantitative models and signals that drive our proprietary trading strategies.

You’ll collaborate closely with traders, data engineers, risk & infrastructure teams to translate market insights into robust algorithmic strategies operating in real-time.

This role demands strong mathematical / statistical / machine-learning foundations as well as ability to handle large datasets, work within low-latency or near-real-time environments, and deliver actionable models that integrate into a trading infrastructure.

Responsibilities

• Research and identify new trading signals and patterns in market data (both structured and unstructured) that can drive systematic strategies.
• Develop quantitative models (statistical, ML / deep learning, econometric, time-series, microstructure) to forecast / detect market-behaviour, price-movement, liquidity events, etc.
• Back-test models on historical and live data, evaluate performance, robustness, risk characteristics and model degradation over time.
• Work with data-engineering and infrastructure teams to deploy models into production / trading environment (including real-time signal generation, monitoring, and feedback loops).
• Collaborate with trading desk to integrate quantitative insight into trading strategies — support signal interpretation, parameter tuning, strategy execution and risk oversight.
• Monitor model performance, drift, regime-changes and real-world execution impacts; conduct post-trade analytics to refine and enhance models.
• Build and maintain datasets, data-pipelines, and research infrastructure (including feature-engineering, data-quality checks, latency / throughput optimisation).
• Publish research findings internally (and occasionally externally where appropriate) — provide clear documentation of modelling assumptions, limitations, validations and performance metrics.
• Stay abreast of academic and industry developments in quantitative finance, machine‐learning, algorithmic trading, market microstructure and related fields; propose applicable innovations.

Required Qualifications

Preferred Qualifications

What We Offer

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