Quantitative Trading Analyst
$90k - $180k+ Discretionary Corporate Bonus + Incentives
Senior
Bachelor's, Master's, PhD
Boston, MA, USA
HybridFour days in the office per week; one remote day per week.
Quantitative FinanceQuantitative Research
Company Historically Provides H1B Sponsorship
About the job
Requirements
- A bachelor's degree is required; a master's or PhD in Statistics, Mathematics, Economics, Computer Science, Engineering, Finance, or a related quantitative discipline is considered favorably.
- Five to seven years of relevant experience in quantitative trading research, execution analytics, transaction cost analysis, 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, including 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 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.
- Expert-level Python skills for quantitative research, modeling, data engineering, and production-quality analytical development, including understanding object-oriented programming, when to use object-oriented programming versus procedural scripts or functions, and how to structure reusable, maintainable code.
- Proficient SQL skills, including querying data with SELECT, WHERE, ORDER BY, COUNT, SUM, AVG, GROUP BY, HAVING, CASE WHEN, and NULL behavior; understanding joins, cardinality, duplicate handling, joins versus subqueries, window functions, and subqueries or common table expressions.
- Fluency with 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, including explaining complex analytical findings clearly, tailoring messages to technical and non-technical audiences, and providing 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.
- Proficient knowledge in at least one of electronic trading, transaction cost analysis, or financial markets knowledge, particularly fixed income market structure, with competent understanding and interest in developing the other areas. Candidates should understand how trading workflows, order lifecycle, RFQ protocols, dealer behavior, liquidity formation, best execution, and execution or pricing algorithms connect to research and analytics problems.
Responsibilities
- Conduct empirical research on trading behavior, market microstructure, liquidity, and execution performance across global fixed income markets; translate business questions into testable hypotheses and develop statistical, optimization, and machine learning models to improve execution decisions and investment outcomes.
- Design and enhance the fixed income transaction cost analysis 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, and recommend enhancements to trading capabilities.
- Build research and analytical tools to 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 execution strategy, trading technology, and research capabilities.
Desired Qualifications
- A master's or PhD in Statistics, Mathematics, Economics, Computer Science, Engineering, Finance, or a related quantitative discipline is favorably considered.
- Experience supporting fixed income trading across Investment Grade Credit, High Yield Credit, Emerging Markets Debt, Securitized Credit, or Agency Mortgages is preferred.
- Interest in applying machine learning, natural language processing, or modern artificial intelligence techniques to trading research, predictive analytics, data quality, automation, or decision support.
- 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.
About the company
Wellington Management is a global investment management firm that offers equity, fixed income, multi-asset, and alternative investments to both institutional clients (such as pension funds, endowments, foundations, and insurers) and individual investors. It manages assets on behalf of clients, earning fees based on assets under management (AUM) and on investment performance. The firm relies on deep research capabilities and market insights to tailor investment strategies that meet client needs, and it integrates environmental, social, and governance (ESG) factors into its process. What sets Wellington Management apart is its broad suite of investment options combined with a commitment to ESG integration and a client-tailored approach, supported by a culture that emphasizes diversity and inclusion. The company’s goal is to help clients achieve their investment objectives by delivering disciplined, research-driven strategies and sustainable investing over the long term.
Company Size
1,001-5,000
Company Stage
N/A
Total Funding
$65.7B
Headquarters
Boston, Massachusetts
Founded
1933
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Simplify's Take
What believers are saying
- Hartford Funds acquisition closes in first quarter 2027, adding wealth distribution.
- The new Wellington-Vanguard-Blackstone solutions expand access to public-private portfolios.
- Wellington’s venture, biotech, climate, and CLO launches broaden fee-bearing alternatives.
What critics are saying
- Hartford Funds integration carries execution risk until regulators approve the Q1 2027 close.
- Steve Klar’s retirement in June 2026 exposes client-platform succession risk.
- Passive giants and private-credit rivals compress Wellington’s fees and threaten AUM growth.
What makes Wellington Management unique
- Wellington blends public and private markets through alliances with Vanguard and Blackstone.
- Its $1.35 trillion platform spans 60 countries and over 2,500 institutional clients.
- Matt Witheiler’s Forbes Midas List streak validates Wellington’s late-stage growth investing credibility.
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Benefits
- Comprehensive health coverage
- Work-life balance
- Financial future
- Development