THE TEAM 

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. 

Rather than simply measuring trading performance, our mission is to continuously improve it. 

We develop the data, models, and research that help answer questions such as: 

• Which execution strategy should be used?  

• When should an order be executed?  

• How much should we expect a trade to cost?  

• What execution price should we be willing to pay?  

• Which dealers consistently deliver the best outcomes?  

• How can systematic trading strategies improve client alpha?  

As our trading platform continues to evolve, the team is expanding beyond traditional transaction cost analysis into predictive modeling, execution optimization, and AI-assisted trading research. 

THE ROLE AND WHAT YOU'LL DO 

We are seeking a Quantitative Trading Analyst to join Trading Research & Analytics and help improve execution outcomes through research, analytics, and systematic decision support. 

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. 

QUALIFICATIONS 

Education & Experience 

• Bachelor’s degree required; master’s or PhD in Statistics, Mathematics, Economics, Computer Science, Engineering, Finance, or a related quantitative discipline considered favorably. 

• 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. 

• Hands-on experience supporting institutional fixed income trading, preferably across Investment Grade Credit, High Yield Credit, Emerging Markets Debt, Securitized Credit, or Agency Mortgages. 

Quantitative Research & Modeling 

• Proficient capability in analytics and quantitative research, with experience cleaning messy real-world data, developing visualizations, generating reports, and using descriptive analytics to explain what happened and diagnostic analytics to explain why it happened. 

• Ability to move beyond reporting into research: formulate trading, execution, liquidity, and portfolio implementation questions as testable hypotheses using statistics, algebra, mathematical reasoning, and data-driven inference. 

• Hands-on experience building, validating, and interpreting predictive models, transaction cost models, optimization frameworks, machine learning models, or AI-assisted research workflows used in trading or execution analytics. 

• Experience analyzing real-world trading datasets, including orders, executions, quotes, dealer responses, benchmarks, prices, liquidity signals, and portfolio attributes. 

• Interest in applying machine learning, natural language processing, or modern AI techniques to trading research, predictive analytics, data quality, automation, or decision support. 

Technical Skills 

Required: 

• Expert-level Python skills for quantitative research, modeling, data engineering, and production-quality analytical development, including solid understanding of object-oriented programming, when to use OOP versus procedural scripts/functions, and how to structure reusable, maintainable code. 

• Proficient SQL skills, including fundamentals of querying data with SELECT, WHERE, ORDER BY, COUNT, SUM, AVG, GROUP BY, HAVING, CASE WHEN, and NULL behavior; strong understanding of joins, cardinality, duplicate handling, joins versus subqueries, window functions, and subqueries/CTEs. 

• Fluency with modern Python research and modeling libraries, including pandas for real-world and messy data, NumPy for vectorized thinking, and scikit-learn for machine learning workflows. 

• Experience using Git, reproducible research workflows, code review, testing, and documentation to maintain analytical rigor and reliability. 

• Proficient time management and prioritization, demonstrated through evidence-based examples of organizing work, protecting focus during interruptions, managing competing priorities, and delivering reliably in a fast-moving trading environment. 

• Proficient communication skills, with the ability to explain complex analytical findings clearly, tailor messages to technical and non-technical audiences, and provide evidence-based examples of effective stakeholder communication. 

• Expert collaboration skills, including acting on feedback, providing constructive feedback to colleagues, sharing knowledge, contributing to collective learning, and constructively handling differences of opinion in ways that strengthen team dynamics. 

Preferred: 

• Experience with JIRA, or business intelligence tools such as Tableau is preferred; these skills can be developed on the job where the candidate demonstrates strong learning agility. 

• Additional experience with APIs, FIX protocol, cloud data platforms, distributed data processing, or market/trading data infrastructure is beneficial. 

Trading & Market Knowledge 

Proficient knowledge in at least one of the following areas, with competent understanding and genuine interest in developing the others: electronic trading, transaction cost analysis, or financial markets knowledge, particularly fixed income market structure. Candidates should understand how trading workflows, order lifecycle, RFQ protocols, dealer behavior, liquidity formation, best execution, and execution/pricing algorithms connect to practical research and analytics problems.