PhD Quant - Selby Jennings

eFinancialCareers; Royal Borough of Greenwich; On-site; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Collaborative env

Large datasets

Incentives

Career growth

Learning opportunities

Competitive pay

Job summary

eFinancialCareers is seeking an exceptional Quant Researcher to join a collaborative, research-driven systematic investment team. The role suits candidates nearing or with a PhD in quantitative fields and requires strong programming in Python/C++/Java.

You will develop predictive signals and systematic strategies across global markets, leveraging large datasets and advanced analytics. You will work with researchers, PMs, and engineers to productionize ideas, backtest rigorously, and continuously

Qualifications

• PhD completed or near completion in a quantitative discipline.
• Outstanding academic record from a leading university.
• Strong background in probability, statistics, optimisation, ML or related quantitative methods.
• Excellent programming skills in Python, C++, Java, or similar.
• Experience with large datasets and empirical research.
• Strong analytical and problem-solving abilities.
• Ability to communicate complex concepts to technical and non-technical audiences.
• Demonstrated intellectual curiosity and passion for quantitative problems.

Responsibilities

• Conduct original quantitative research to identify alpha-generating opportunities across global markets.
• Develop predictive models using advanced statistical techniques, ML methods, and data-driven approaches.
• Analyse large structured and unstructured datasets to uncover patterns and signals.
• Design, implement, and evaluate systematic trading strategies with backtesting and validation.
• Collaborate with researchers, PMs, and engineers to move ideas into production.
• Improve models through ongoing performance analysis and refinement.
• Research and evaluate new datasets and modelling techniques.
• Build scalable research tools and infrastructure for strategy development.
• Present research findings to senior stakeholders clearly.
• Stay at the forefront of statistics, ML, AI, and quantitative finance.

Skills

Python

C++

Java

Machine learning

Statistics

Data analysis

Communication

Education

PhD in Mathematics/Statistics/CS/Physics/Engineering/ML/OR

Tools

Distributed computing

Cloud/data processing

Backtesting frameworks

Job description

Overview

We are seeking an exceptional Quant Researcher to join a highly collaborative and research-driven systematic investment team. This role is ideally suited to candidates completing or recently having completed a PhD in a quantitative discipline such as Mathematics, Statistics, Computer Science, Physics, Engineering, Machine Learning, Operations Research, or a related field.

You will be responsible for identifying, researching, and developing predictive signals and systematic trading strategies across global financial markets. The successful candidate will combine strong theoretical foundations with practical programming expertise to transform complex datasets into innovative investment insights.

Key Responsibilities

• Conduct original quantitative research to identify alpha-generating opportunities across global markets.
• Develop predictive models using advanced statistical techniques, machine learning methods, and data-driven approaches.
• Analyse large, structured and unstructured datasets to uncover patterns and investment signals.
• Design, implement, and evaluate systematic trading strategies through rigorous backtesting and validation frameworks.
• Collaborate closely with quantitative researchers, portfolio managers, and engineers to move research ideas into production.
• Improve existing models through ongoing performance analysis, monitoring, and refinement.
• Research and evaluate new datasets, alternative data sources, and novel modelling techniques.
• Build scalable research tools and infrastructure to support efficient strategy development.
• Present research findings and recommendations to senior stakeholders in a clear and concise manner.
• Stay at the forefront of developments in statistics, machine learning, artificial intelligence, and quantitative finance.

Required Qualifications

• PhD (completed or near completion) in Mathematics, Statistics, Computer Science, Physics, Engineering, Machine Learning, Econometrics, Operations Research, or a related quantitative discipline.
• Outstanding academic record from a leading university.
• Strong background in probability, statistics, optimisation, machine learning, or related quantitative methods.
• Excellent programming skills in Python, C++, Java, or a similar language.
• Experience working with large datasets and conducting empirical research.
• Strong analytical and problem-solving abilities.
• Ability to communicate complex concepts effectively to both technical and non-technical audiences.
• Demonstrated intellectual curiosity and a passion for solving challenging quantitative problems.

Preferred Experience

• Research experience in machine learning, deep learning, reinforcement learning, statistical modelling, time-series analysis, optimisation, or artificial intelligence.
• Publications in leading academic conferences or journals.
• Experience with distributed computing, high-performance computing, or large-scale data processing frameworks.
• Exposure to quantitative finance, systematic investing, or algorithmic trading is advantageous but not required.
• Familiarity with cloud computing, data engineering, or production-grade software development practices.

What We Offer

• Opportunity to work alongside world-class researchers, engineers, and investment professionals.
• Access to extensive datasets, cutting-edge technology, and substantial computational resources.
• A highly collaborative, intellectually stimulating research environment.
• Direct impact on live investment decisions and trading strategies.
• Exceptional opportunities for learning, development, and long-term career progression.
• Competitive compensation package, including performance-based incentives.