Quantitative Trading Analyst

Wellington Management•Boston, MA

1d•$90,000 - $180,000•Hybrid

About The Position

The Investment Implementation & Trading department at Wellington Management transforms investment decisions into high-quality portfolio implementation across global markets. This department integrates portfolio construction, global trading, trading research, treasury, middle office, and trading risk management to enhance execution quality, trading efficiency, and investment outcomes. Wellington's internally developed trading technology and proprietary data platforms provide a competitive edge and are foundational to its research and electronic trading strategy. The Trading Research & Analytics (TRA) team serves as the quantitative research and analytics function for Wellington's global trading organization. The team collaborates directly with traders, portfolio managers, broker-dealers, and clients to improve execution outcomes through quantitative research, data science, and systematic trading analytics. Their work is situated at the intersection of electronic trading, transaction cost analysis, and market microstructure, with a mission to continuously improve trading performance. The team develops the data, models, and research to address critical questions such as optimal execution strategy, order execution timing, expected trade costs, acceptable execution prices, dealer performance evaluation, and the impact of systematic trading strategies on client alpha. As the trading platform evolves, the TRA team is expanding its focus beyond traditional transaction cost analysis to include predictive modeling, execution optimization, and AI-assisted trading research.

Requirements

Bachelor’s degree; 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.
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.
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.
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.

Nice To Haves

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.

Responsibilities

Conduct empirical research on trading behavior, market microstructure, liquidity, and execution performance across global fixed income markets.
Translate ambiguous business questions into testable research hypotheses and develop statistical, optimization, and machine learning models that improve execution decisions and investment outcomes.
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.
Partner with fixed income traders to analyze execution across Global Investment Grade Credit, Global High Yield, Emerging Markets Debt, Securitized Credit, and Agency Mortgages.
Track market structure, electronic trading, dealer behavior, RFQ protocols, and liquidity trends to recommend enhancements to Wellington’s trading capabilities.
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.
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.
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.

Benefits

retirement plan
health and wellbeing
dental
vision
pharmacy coverage
health savings account
flexible spending accounts
commuter program
employee assistance program
life and disability insurance
adoption assistance
back-up childcare
tuition/CFA reimbursement
paid time off (leave of absence, paid holidays, volunteer, sick and vacation time)