Quantitative Researcher

Durlston Partners LLP; Dubai; On-site; AED 600,000 - 900,000; Full time

Benefits offered by this job

Relocation support

Visa sponsorship

Health cover

Meals

Job summary

Durlston Partners LLP seeks a Quant Researcher for mid-frequency equities in Dubai, to join a small, high-impact team building systematic strategies with intraday to multi-day holding periods.

You will generate alpha ideas, build signals, construct portfolios and model execution, then manage live strategies with ownership of results.

Qualifications

• 2+ years in systematic equity research at a prop trading firm, quant hedge fund or similar.
• Advanced degree (PhD or MSc) in mathematics, statistics, physics, CS or engineering, from a top university.
• Strong statistics and machine learning skills applied to noisy financial data: regression, time series, regularisation and cross-validation.
• Excellent Python skills (pandas, NumPy and the scientific stack). C++ or similar is a plus.
• Solid understanding of equity market structure, factor models, risk management and execution costs.
• Experience handling large, messy datasets and building robust research pipelines.
• An intellectually honest, PnL-focused mindset, independence and strong ownership.

Responsibilities

• Research and develop predictive signals for global equities from price and volume, order book, fundamental, event-driven and alternative datasets.
• Build and improve portfolio construction, risk models and transaction-cost and market-impact models.
• Run rigorous backtests, guarding against overfitting, lookahead bias and data-snooping.
• Bring strategies into production and monitor live performance, attribution and decay.
• Find, evaluate and onboard new datasets to extend existing alphas and create new ones.
• Work closely with engineers and other researchers to improve shared research infrastructure.

Skills

Python (Pandas/NumPy)

Machine learning

Statistical analysis

Data handling

C++ knowledge

Education

PhD or MSc in quantitative field

Tools

KDB+/Q

Distributed computing

Job description

Quant Researcher, Mid-Frequency Equities

Firm: Leading global proprietary trading firm

About the firm

Our client is a highly successful proprietary trading firm that trades only its own capital across global markets. It pairs world-class technology with a rigorous, scientific approach to research. Teams are small, collaborative and flat, and researchers have real ownership of strategies and a direct link to PnL.

The role

You'll join a small, high-impact team building systematic equity strategies with holding periods from intraday to several days. The role covers the full research cycle: generating alpha ideas, building signals, constructing portfolios and modelling execution, then managing strategies once they're live.

What you'll do

• Research and develop predictive signals for global equities from price and volume, order book, fundamental, event-driven and alternative datasets.
• Build and improve portfolio construction, risk models and transaction-cost and market-impact models.
• Run rigorous, statistically sound backtests, guarding against overfitting, lookahead bias and data-snooping.
• Bring strategies into production and monitor live performance, attribution and decay.
• Find, evaluate and onboard new datasets to extend existing alphas and create new ones.
• Work closely with engineers and other researchers to improve shared research infrastructure.

What we're looking for

• 2+ years in systematic equity research at a prop trading firm, quant hedge fund or similar. A track record of live, profitable strategies is strongly preferred.
• An advanced degree (PhD or MSc) in a quantitative field such as mathematics, statistics, physics, computer science or engineering, from a top university.
• Strong statistics and machine learning skills applied to noisy financial data: regression, time series, regularisation and cross-validation.
• Excellent Python skills (pandas, NumPy and the scientific stack). C++ or similar is a plus.
• A solid understanding of equity market structure, factor models, risk management and execution costs.
• Experience handling large, messy datasets and building robust research pipelines.
• An intellectually honest, PnL-focused mindset, independence and strong ownership.

Nice to have

• Experience with alternative data: sentiment, NLP, supply chain, web or app data.
• Experience across regions, such as the US, Europe and APAC.
• Experience with kdb+/q or distributed computing frameworks.
• Experience building portfolio optimisers or execution algorithms.

What's on offer

• Highly competitive compensation: market-leading base plus a significant discretionary or PnL-linked bonus.
• Direct ownership of strategies, with clear visibility of your contribution to PnL.
• Best-in-class data, computing resources and research infrastructure.
• A collaborative, non-hierarchical culture alongside some of the strongest researchers in the industry.
• [Benefits: relocation support, visa sponsorship, health cover, meals, etc.]