Quantitative Researcher - Systematic Credit & Macro

Location: London, UK
Start Date: Flexible. Candidates with immediate availability through to longer notice periods will be considered.

Overview

A leading quantitative investment team is seeking a Quantitative Researcher to join a high-performing systematic trading group focused on credit and macro markets. This is an opportunity to work in a small, entrepreneurial research environment alongside an experienced Portfolio Manager, contributing directly to the development of alpha-generating investment strategies across global markets.

The role offers exposure to the entire research and investment lifecycle, from idea generation and signal discovery through to portfolio construction, implementation, and live monitoring. The team places a strong emphasis on rigorous scientific thinking, creativity, and data-driven decision-making. While prior experience in systematic credit or macro investing is advantageous, the primary focus is on identifying exceptional researchers with strong quantitative foundations, intellectual curiosity, and a demonstrated ability to solve complex problems.

This position is particularly well suited to researchers who enjoy working with large datasets, conducting empirical analysis, developing predictive models, and translating research insights into real trading applications.

Key Responsibilities

Alpha Research & Signal Development

• Conduct original quantitative research to identify and develop new sources of alpha across credit and macro markets.
• Formulate and test investment hypotheses based on financial theory, market structure understanding, economic intuition, and empirical evidence.
• Generate systematic trading signals using statistical, mathematical, and machine learning techniques where appropriate.
• Evaluate academic research and industry literature to identify opportunities for novel strategy development.
• Investigate relationships between market data, economic indicators, pricing dynamics, and risk premia.

Data Analysis & Alternative Data Research

• Source, clean, validate, and analyse large-scale financial and alternative datasets.
• Design robust data pipelines and research frameworks to support signal development.
• Extract predictive insights from structured and unstructured datasets.
• Assess data quality, identify biases, and ensure statistical robustness of findings.
• Explore innovative data sources that may contribute to investment decision-making.

Model Development & Backtesting

• Build, maintain, and improve quantitative models used for signal generation and portfolio construction.
• Design and implement rigorous backtesting methodologies to evaluate strategy performance.
• Analyse risk-adjusted returns, turnover, transaction costs, capacity constraints, and portfolio characteristics.
• Perform sensitivity testing, scenario analysis, and out-of-sample validation to assess model robustness.
• Collaborate closely with senior investment professionals to refine and validate research outputs.

Portfolio & Investment Process Support

• Contribute directly to the systematic investment process through ongoing research and model enhancement.
• Monitor live strategies and investigate performance drivers.
• Assist in portfolio optimisation and risk management initiatives.
• Identify market regime changes and evaluate their impact on existing models and signals.
• Support the integration of research ideas into production trading systems.

Technology & Research Infrastructure

• Write high-quality, scalable, and well-documented Python code within a collaborative team environment.
• Develop reusable research tools, analytical libraries, and workflow automation solutions.
• Improve research infrastructure and contribute to best practices around testing, version control, and code quality.
• Work closely with technology and infrastructure teams where required to support production deployment.

Communication & Collaboration

• Present research findings clearly and concisely to portfolio managers and fellow researchers.
• Translate technical concepts into actionable investment insights.
• Participate in collaborative idea generation sessions and research reviews.
• Incorporate feedback quickly and continually refine research approaches.
• Contribute to a culture of intellectual rigor, open discussion, and continuous improvement.

Required Qualifications

Education

• Master's degree or PhD in a quantitative discipline such as:
  • Mathematics
  • Statistics
  • Physics
  • Computer Science
  • Engineering
  • Financial Engineering
  • Econometrics
  • Operations Research
  • Related quantitative fields

Experience

• Approximately 2+ years of experience in a quantitative research, systematic investing, quantitative analytics, or related role.
• Experience within buy-side or sell-side environments is beneficial but not mandatory.
• Demonstrated track record of conducting rigorous empirical research and working with financial datasets.
• Exposure to systematic strategy development, quantitative modelling, or predictive analytics.

Technical Skills

• Strong programming skills in Python.
• Advanced data analysis and statistical modelling capabilities.
• Experience working with large and complex datasets.
• Understanding of statistical inference, hypothesis testing, time series analysis, and predictive modelling techniques.
• Ability to build reproducible research workflows and robust backtesting frameworks.

Research Mindset

• Exceptional analytical and problem-solving skills.
• Strong intellectual curiosity and willingness to challenge existing assumptions.
• Ability to independently investigate complex research questions.
• Evidence of academic excellence, publications, competitions, significant personal research projects, or other demonstrations of intellectual achievement.
• Strong attention to detail and commitment to research integrity.

Communication Skills

• Excellent written and verbal communication abilities.
• Capable of explaining sophisticated quantitative concepts to both technical and non-technical audiences.
• Comfortable operating in a fast-paced investment environment with evolving priorities.
• Strong collaboration skills and ability to work effectively within a small team.

Preferred Qualifications

Candidates may possess some or all of the following:

• Experience researching or trading systematic credit strategies.
• Knowledge of corporate bond markets, credit derivatives, or credit risk modelling.
• Exposure to macro investing, rates markets, interest rate products, futures, FX, or cross-asset strategies.
• Experience working with alternative datasets and non-traditional data sources.
• Familiarity with portfolio construction and optimisation techniques.
• Understanding of machine learning methodologies applied to financial markets.
• Experience with cloud computing, distributed data processing, or large-scale research environments.
• Knowledge of market microstructure and transaction cost analysis.
• Familiarity with quantitative risk models and factor-based investing approaches.

What Makes This Opportunity Attractive?

• Direct partnership with an experienced Portfolio Manager.
• Significant ownership and responsibility from day one.
• Exposure to the full quantitative investment lifecycle.
• Opportunity to work on both credit and macro investment strategies.
• Small-team environment where individual contributions have a meaningful impact.
• Strong emphasis on innovation, creativity, and independent thinking.
• Access to large datasets, sophisticated research tools, and institutional investment infrastructure.
• Meritocratic culture focused on research quality and investment outcomes.

Ideal Candidate Profile

The ideal candidate is a highly analytical researcher who combines strong quantitative skills with genuine curiosity about markets. They enjoy tackling difficult problems, working with data, developing new ideas, and challenging conventional thinking. Whether coming from a systematic investing background, academia, data science, physics research, or another highly quantitative field, they are motivated by the opportunity to apply rigorous research methods to real-world financial markets and contribute directly to investment performance.