Quant Researcher - FI

About the Company

Our client is a boutique investment firm that combines quantitative research, proprietary data infrastructure, and advanced analytics to support investment decision-making across fixed income markets.

The firm has invested heavily in building an in-house research environment that integrates high-quality market data, rigorous analytical frameworks, and AI-enabled workflows to generate differentiated investment insights.

The investment team places a strong emphasis on data integrity, research discipline, and robust validation processes. Researchers work closely with senior investment professionals and play a direct role in determining which analytical findings ultimately influence portfolio construction and capital allocation decisions.

The Opportunity

This role sits at the heart of the firm's quantitative research process and serves as the primary expert on fixed income data quality, analytical standards, and model validation. Unlike traditional quantitative research positions focused on alpha generation or model development, this role is responsible for ensuring that research outputs, data inputs, and model conclusions are statistically robust, economically meaningful, and suitable for real-world implementation.

You will work closely with quantitative researchers, senior investment professionals, and technology teams to define analytical standards, validate datasets, assess model outputs, and maintain the frameworks that determine whether investment signals are suitable for deployment. The position offers significant exposure to fixed income markets, quantitative research methodologies, and portfolio management decision-making.

Key Responsibilities

Fixed Income Data Expertise

• Act as the firm's subject matter expert on fixed income datasets and market analytics.
• Define analytical requirements for the data structures used by quantitative research teams.
• Specify how returns, spreads, ratings, fundamentals, call schedules, and benchmark data should be constructed and maintained.
• Ensure data is correctly aligned to decision dates and reflects point-in-time information.
• Establish standards governing the handling of issuer events and fixed income security characteristics.

Data Validation and Quality Control

• Design and implement rigorous validation procedures for fixed income datasets.
• Reconcile datasets against independent sources and identify discrepancies.
• Develop systematic checks to identify data gaps, pricing anomalies, and inconsistencies before they impact research outputs.
• Monitor ongoing data quality and ensure analytical datasets maintain the highest standards of accuracy.
• Work closely with technology teams to address issues identified through validation processes.

Backtesting and Research Evaluation

• Own and enhance the firm's research evaluation and backtesting framework.
• Ensure research methodologies eliminate look-ahead bias and maintain point-in-time integrity.
• Design realistic transaction-cost assumptions and implementation frameworks.
• Evaluate investment strategies under varying market conditions, including stress environments.
• Assess turnover, liquidity considerations, capacity constraints, and implementation feasibility.
• Produce detailed performance reports that accurately reflect real-world investment conditions.

Quantitative Model Validation

• Establish and maintain the firm's quantitative model validation standards.
• Independently assess machine learning and statistical models before deployment.
• Evaluate the statistical robustness and practical relevance of research outputs.
• Review model assumptions, target construction, calibration, and predictive performance.
• Identify weaknesses, potential biases, and implementation risks within research findings.
• Work collaboratively with machine learning researchers to improve model quality and reliability.

Reporting and Stakeholder Engagement

• Produce clear and detailed validation documentation for senior stakeholders.
• Present research findings, validation outcomes, and recommendations to senior researchers and investment professionals.
• Provide practical guidance on whether model outputs should be incorporated into investment decision-making.
• Communicate technical findings to both specialist and non-specialist audiences.

Production and Governance

• Ensure consistency between research environments and live production outputs.
• Support reproducibility, version control, monitoring, and governance standards across research workflows.
• Contribute to the development of best practices covering data quality, model validation, and research governance.

Candidate Requirements

Education

Applicants should possess:

• MSc degree in Finance, Economics, Statistics, Mathematics, Physics, Engineering, Computer Science, or another highly quantitative discipline.
• Equivalent industry experience may also be considered.

Professional Experience

• 2-4 years of professional experience working with security-level fixed income datasets.
• Experience within asset management, hedge funds, investment banks, sell-side research, index providers, pricing vendors, or related financial institutions.
• Proven responsibility for ensuring data quality, integrity, and correctness rather than solely consuming data outputs.
• Experience supporting research, analytics, strategy, or quantitative investment functions.

Fixed Income Markets Knowledge

Strong understanding of:

• Corporate and sovereign bond markets.
• Duration, convexity, spread measures, and fixed income risk analytics.
• Option-adjusted spreads and callable bond structures.
• Bond pricing conventions and return construction methodologies.
• Security identifiers including ISIN, CUSIP, and FIGI.
• Corporate actions and fixed income lifecycle events.

Quantitative and Statistical Skills

Knowledge of:

• Calibration techniques and probabilistic forecasting.
• Cross-validation approaches for panel and time-series datasets.
• Point-in-time data principles and prevention of look-ahead bias.
• Strategy evaluation methodologies and performance measurement.
• Model validation frameworks and research governance disciplines.
• Familiarity with credit deterioration modelling and interpretability approaches is advantageous.

Technical Skills

Strong experience with:

• Python, including analytical libraries such as Pandas, NumPy, or Polars.
• SQL and database querying.
• Git, GitLab, GitHub, or equivalent version-control tools.
• Software testing and validation principles.
• Research and analytical workflow management.

Desired Attributes

The ideal candidate will demonstrate:

• Exceptional attention to detail.
• A highly analytical and methodical approach to problem-solving.
• Confidence challenging assumptions and research conclusions when warranted by data.
• Strong written communication skills.
• Intellectual curiosity and a genuine interest in financial markets.
• The ability to work effectively within a small, high-performing team environment.
• A commitment to maintaining the highest standards of research integrity and analytical rigour.

What Makes This Role Unique?

This role offers a rare opportunity to influence investment outcomes without being directly responsible for generating alpha signals. Instead, you will serve as the independent analytical authority responsible for ensuring that data, models, and research outputs meet the standards required for real capital deployment.

You will gain exposure to advanced quantitative research, machine learning validation, fixed income analytics, and portfolio management while working alongside experienced investment professionals in a collaborative and intellectually demanding environment.

For candidates seeking a highly analytical role that combines fixed income expertise, data governance, quantitative research evaluation, and meaningful investment impact, this position represents an outstanding opportunity to develop deep expertise at the intersection of finance, data, and technology.