Quantitative Trading Analyst

About the Role

The DEPARTMENT Investment Implementation & Trading transforms investment decisions into high‑quality portfolio implementation across global markets. The department brings together portfolio construction, global trading, trading research, treasury, middle office, and trading risk management to improve execution quality, trading efficiency and investment outcomes.

The Trading Research & Analytics (TRA) team is the quantitative research and analytics function supporting Wellington’s global trading organization. We partner directly with traders, portfolio managers, broker‑dealers and clients to improve execution outcomes through quantitative research, data science and systematic trading analytics. Our work sits at the intersection of electronic trading, transaction cost analysis and market microstructure.

Quantitative Trading Research

Conduct empirical research on trading behaviour, market microstructure, liquidity and execution performance across global fixed‑income markets. Translate ambiguous business questions into testable research hypotheses and develop statistical, optimisation and machine‑learning models that improve execution decisions and investment outcomes.

Transaction Cost Analysis & Execution Analytics

Design and enhance Wellington’s fixed‑income TCA framework, including pre‑ and post‑trade cost models, implementation shortfall analytics, dealer scorecards, execution benchmarking and best‑execution reporting. Use quantitative analysis to identify practical opportunities to improve execution quality.

Fixed‑Income Market‑Structure Research

Partner with fixed‑income traders to analyse execution across Global Investment Grade Credit, Global High Yield, Emerging Markets Debt, Securitised Credit and Agency Mortgages. Track market structure, electronic trading, dealer behaviour, RFQ protocols and liquidity trends to recommend enhancements to Wellington’s trading capabilities.

Systematic Trading & Decision Support

Build research and analytical tools that help traders and portfolio managers make better execution decisions. Evaluate execution strategies, dealer selection, liquidity conditions and timing to support systematic trading workflows and improve outcomes.

Trading Data & Research Platform

Own the analytical representation of trading data and partner with technology teams to build scalable research datasets across the trading lifecycle. Maintain production‑quality research infrastructure that combines OMS, EMS, market, pricing and portfolio data with strong data quality, business logic, reproducibility and analytical integrity.

Partnership & Influence

Collaborate with portfolio managers, traders, broker‑dealers, technology teams and senior leaders to translate research into practical trading decisions. Communicate complex findings clearly and help shape Wellington’s execution strategy, trading technology and research capabilities.

• Education & Experience – Bachelor’s degree required; master’s or PhD in Statistics, Mathematics, Economics, Computer Science, Engineering, Finance or related quantitative disciplines is an advantage. 5–7 years of relevant experience in quantitative trading research, execution analytics, TCA, systematic trading, quantitative investment research or advanced analytics in an institutional markets environment.
• Fixed‑Income Trading Experience – Hands‑on experience supporting institutional fixed‑income trading, preferably across Investment Grade Credit, High Yield Credit, Emerging Markets Debt, Securitised Credit or Agency Mortgages.
• Quantitative Research & Modelling – Proficiency in cleaning messy real‑world data, developing visualisations, generating reports and using descriptive analytics to explain what happened; able to formulate trading, execution, liquidity and portfolio implementation questions as testable hypotheses using statistics, algebra, mathematical reasoning and data‑driven inference.
• Predictive & Machine‑Learning Modelling – Hands‑on experience building, validating and interpreting predictive models, transaction‑cost models, optimisation frameworks, machine‑learning models or AI‑assisted research workflows used…