Job Description

This is an elite, hands-on opportunity to join a Global Leading Firm at the forefront of systematic trading and market-making. The firm operates with a high-performance, technology-first culture, applying advanced statistical methods to maintain a competitive edge across multiple asset classes including Equities, Fixed Income, Commodities, and FX.

As a Quantitative Researcher, you will be the architect of the firm’s trading signals. You won’t just be analyzing data; you’ll be hunting for “alpha”—the elusive statistical patterns that indicate future market movements. This role requires a unique blend of scientific rigor and engineering practicality, as you will be responsible for translating complex mathematical insights into production-grade code that powers live global markets.

What You’ll Work On

Signal Discovery: Conduct high-fidelity research and statistical analysis to build predictive models for trading signals.
Large-Scale Data Science: Navigate and validate patterns within massive, noisy datasets, often incorporating alternative or unconventional data sources.
Model Innovation: Refine valuation frameworks and push the boundaries of machine learning (including Deep Learning) in financial time-series contexts.
Algorithm Deployment: Implement complex mathematical algorithms into efficient, production-grade systems using Python and compiled languages.
Backtesting & Execution: Run rigorous simulations of strategies to evaluate performance and assist in the seamless deployment into live production environments.

Required Qualifications & Experience

Education: Advanced degree (PhD or Master’s) in a highly quantitative field such as Physics, Mathematics, Statistics, or Computer Science.
Technical Mastery: Solid foundation in probability, statistics, and practical application of Machine Learning/Deep Learning.
Industry Track Record: Proven success in alpha generation within a buy-side quant firm or a major sell-side trading desk.
Programming: Expert-level Python skills are mandatory; experience with C++ or other compiled languages for performance-critical components is a plus.
Communication: Ability to distill highly complex research findings into clear, actionable strategies for traders and non-technical stakeholders.

Job Data Table

Category	Details
Company Name	HUG Recruitment (on behalf of a Global Leading Trading Firm)
Location	London, New York, and Asia (Remote options discussed)
Locality	City of London / Manhattan / Singapore & Hong Kong
Job Type	Full-time
Base Salary	£130,000 – £200,000 (depending on location and level)
Bonus	Competitive, performance-linked, top-of-market
Primary Skill	Quantitative Research & Alpha Generation
Secondary Skill	Machine Learning (ML/DL) & Production Python

Skills & Competency Table

Key Skill	Proficiency Level
Statistical Analysis & Probability	Expert
Python (Numerical/Data Stack)	Expert
Alpha Signal Generation	Expert
Machine Learning & Deep Learning	Advanced
Production-Grade Coding	Advanced
Backtesting & Simulation	Expert

Salaries & Compensation Analysis (2026 Estimates)

Region	Est. Base Salary	Est. Total Compensation (TC)	Notes
London	£110k – £150k	£250k – £550k+	High performance-to-bonus ratio.
New York	$175k – $275k	$400k – $800k+	Typically the highest total comp globally.
Asia (HK/SG)	$150k – $220k	$300k – $600k+	Significant focus on low-tax benefits.

Job Summary

This elite “Buy-Side” role involves creating predictive models and trading signals for a global market-making firm. You will handle large-scale datasets, develop ML-driven valuation models, and implement production code. Requires a PhD/MS, expert Python skills, and a proven track record of alpha generation in professional trading environments.

5 FAQs

Who is HUG Recruitment? HUG is a specialized London-based digital and finance recruitment agency known for a “social impact” model that reinvests hiring profits back into client employee benefits.
Is this a junior or senior role? The posting targets “strong performers” with “proven success,” suggesting it is intended for mid-to-senior levels (3–7+ years experience).
Does the firm use specific ML techniques? Yes, the role specifically looks for practitioners in Deep Learning applied to financial time-series data.
Will I work in an office? The firm has major hubs in London, New York, and Asia; specific hybrid/on-site policies vary by firm but usually require 3-4 days in-office.
What are “unconventional data sources”? These often refer to alternative data like satellite imagery, shipping logs, social media sentiment, or credit card transaction data.

Expert Analysis

In 2026, the “Quant Arms Race” is moving away from simple linear models toward Horizon-Aware Transformers and LLM-based Signal Extraction. This role is a prime example of a firm looking for “Full-Stack Quants”—researchers who can not only find a signal but also possess the engineering chops to ensure that signal isn’t lost to latency or slippage during production implementation.

Career Path

A Quant Researcher at this level typically moves into a Senior Portfolio Manager (PM) role or a Head of Research position. In elite market-making firms, successful quants often build their own “pods” or teams, receiving a direct percentage of the P&L (Profit & Loss) generated by their signals.

To apply for this job please visit uk.linkedin.com.