Quant Researcher - ML

About the Company

Our client is a boutique investment firm at the forefront of quantitative investing, combining advanced machine learning, artificial intelligence, and fundamental research to support investment decision-making. The firm has built a sophisticated in-house data and analytics platform that enables researchers and portfolio managers to leverage large-scale datasets, alternative data sources, and cutting-edge AI technologies to generate investment insights.

Operating within a highly collaborative environment, the firm brings together quantitative researchers, investment professionals, and technology specialists to solve complex problems across financial markets. Researchers have direct exposure to decision-makers and play a meaningful role in shaping investment outcomes. Machine Learning Engineer (Quant).docx [Machine Le...er (Quant) | Word]

The Opportunity

This is a unique opportunity for a Machine Learning Engineer with a strong quantitative background to work on real-world prediction problems within financial markets. The role combines statistical modelling, machine learning research, natural language processing, and large language model applications in a production investment environment.

You will develop predictive models across fixed income and credit markets while also building AI-powered systems that extract and structure information from complex financial documents. Your work will have a direct impact on investment research and portfolio construction, with model outputs consumed by portfolio managers and senior investment professionals.

The successful candidate will operate at the intersection of machine learning research, quantitative analytics, and AI engineering, contributing to both model development and data infrastructure initiatives.

Key Responsibilities

Quantitative Machine Learning Research

• Design, develop, and deploy machine learning models for prediction problems across financial markets.
• Build and maintain predictive models focused on issuer credit deterioration, transaction costs, liquidity forecasting, and relative-value opportunities.
• Apply machine learning techniques to low signal-to-noise datasets where robustness and statistical discipline are critical.
• Conduct extensive out-of-sample testing and validation to ensure model reliability and performance.
• Evaluate model effectiveness using appropriate statistical techniques and predictive performance metrics.
• Develop approaches for handling non-stationary data, structural market changes, and evolving market regimes.
• Design methodologies for modelling rare events and infrequent outcomes.

Model Validation and Research Standards

• Produce comprehensive evidence supporting model validity and research conclusions.
• Work within a rigorous research framework emphasizing reproducibility, explainability, and statistical robustness.
• Support independent validation processes through clear documentation and transparent methodology.
• Ensure models meet high standards for both statistical correctness and practical applicability.
• Implement calibration techniques and uncertainty estimation methods where appropriate.
• Evaluate model behaviour under different market environments and changing economic conditions.

LLM and Document Intelligence Solutions

• Build AI systems that extract structured information from large unstructured financial documents.
• Develop and maintain LLM-powered workflows for processing earnings-call transcripts, filings, prospectuses, legal documents, and market disclosures.
• Create retrieval and extraction frameworks capable of handling long-form documents.
• Design schema-based output structures that enable reliable downstream analysis.
• Measure extraction accuracy using labelled datasets and robust evaluation methodologies.
• Ensure outputs remain traceable, auditable, and linked back to source material.
• Investigate novel applications of generative AI and large language models within quantitative research workflows.

Data Engineering and Infrastructure

• Design and develop scalable data pipelines supporting machine learning and research initiatives.
• Build processes for data ingestion, cleaning, transformation, and feature generation.
• Maintain production-quality systems used in live research and investment environments.
• Collaborate with researchers and engineers to improve data availability and workflow efficiency.
• Support monitoring, maintenance, and performance optimisation of models running in production.
• Contribute to the firm's broader AI and data infrastructure roadmap.

Explainability and Investment Communication

• Generate model attribution and explainability outputs that support investment decision-making.
• Present research findings to quantitative researchers, portfolio managers, and senior stakeholders.
• Communicate complex technical concepts clearly to non-technical audiences.
• Support investment teams in understanding model signals and prediction outputs.
• Produce documentation that can be used in internal governance, investment committee discussions, and regulatory processes.

Collaborative Research

• Work closely with quantitative researchers, data scientists, and investment professionals.
• Participate in idea generation, model development, testing, and research discussions.
• Contribute to a culture of intellectual curiosity, rigorous testing, and continuous improvement.
• Assist in evaluating emerging machine learning techniques and AI technologies.
• Share knowledge and best practices across the research and technology teams.

Required Qualifications

Education

Applicants should possess one of the following:

• MSc or PhD in Machine Learning, Statistics, Mathematics, Physics, Computer Science, Econometrics, Economics, or a related quantitative discipline.
• Equivalent practical experience demonstrating significant quantitative and machine learning expertise.

Professional Experience

• 3 to 5 years of industry experience applying machine learning techniques to structured datasets.
• Demonstrated experience working with panel, tabular, or time-series data.
• Experience developing predictive models in research-intensive environments.
• Proven track record of solving complex quantitative problems using statistical and machine learning techniques.
• Experience operating within highly analytical teams where rigorous validation is required.

Experience from finance is advantageous but not essential. Candidates from insurance, forecasting, scientific research, healthcare, risk analytics, industrial optimisation, or related fields are also encouraged to apply.

Technical Requirements

Machine Learning Expertise

Strong experience in:

• Gradient boosting methods.
• Tree-based ensemble models.
• Regularised regression techniques.
• Supervised learning methodologies.
• Statistical learning theory.
• Predictive modelling.
• Feature engineering.
• Model selection and validation.
• Probability forecasting.
• Calibration techniques.
• Uncertainty quantification.
• Model explainability methods.
• Handling imbalanced datasets.
• Missing data treatment and imputation.
• Distribution shift analysis.
• Time-series modelling.
• Forecasting methodologies.

Advanced Quantitative Methods

Knowledge of:

• Bayesian approaches.
• State-space models.
• Regime-switching frameworks.
• Hierarchical modelling.
• Multi-task learning.
• Survival analysis.
• Hazard models.
• Event-driven modelling.
• Rare-event prediction.
• Statistical inference.
• Experimental design.
• Multiple-testing correction.
• Conformal prediction techniques.

AI and Large Language Models

Experience with:

• Large language models.
• Retrieval-augmented generation.
• Document understanding systems.
• Information extraction.
• Long-context document processing.
• Prompt engineering.
• Structured generation techniques.
• Evaluation framework design.
• NLP pipelines.
• Text analytics.

Programming and Engineering

Strong proficiency in:

• Python.
• SQL.
• Git or equivalent version-control systems.
• Software engineering best practices.
• Data pipeline development.
• Production machine learning systems.
• Testing and validation frameworks.
• API integration and workflow automation.

Familiarity with cloud environments, distributed computing frameworks, and modern machine learning ecosystems is advantageous.

Desired Characteristics

The successful candidate is likely to demonstrate:

• Strong intellectual curiosity.
• Rigorous analytical thinking.
• Exceptional problem-solving abilities.
• Attention to detail.
• High standards of scientific methodology.
• Ability to work independently.
• Strong written and verbal communication skills.
• Interest in financial markets and investing.
• Willingness to challenge assumptions through data and experimentation.
• Collaborative mindset and ability to work with multidisciplinary teams.

What Makes This Role Unique?

This position offers significantly more ownership and research exposure than many traditional machine learning engineering roles. Rather than supporting models behind the scenes, you will work directly with researchers and investment decision-makers to solve meaningful business problems.

The role provides a rare combination of:

• Machine learning research.
• Quantitative finance applications.
• LLM and generative AI deployment.
• Data infrastructure development.
• Direct investment impact.
• Collaborative work with senior stakeholders.

You will join a highly specialised environment where technical excellence, intellectual curiosity, and rigorous scientific thinking are valued. The successful candidate will have the opportunity to influence both research direction and technology strategy while working on challenging real-world problems at the intersection of AI, machine learning, and quantitative investing.