Full-Time
Quantitative Trading Analyst
Trading Research & Analytics
Updated on 9/12/2026
Wellington Management
1,001-5,000 employees
Global asset management for institutional clients
Compensation Overview
$90k - $180k/yr
Company Historically Provides H1B Sponsorship
Boston, MA, USA
Hybrid
Four days on-site per week with flexibility to work remotely one day per week.
Bachelor's, Master's, PhD
 Quantitative Finance (1) |
|---|
Get referred to Wellington Management
See people who can refer or advise you
- A bachelor's degree is required.
- 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 is required.
- Hands-on experience supporting institutional fixed income trading is required.
- Proficient capability in analytics and quantitative research, including cleaning messy real-world data, developing visualizations, generating reports, and applying descriptive and diagnostic analytics, is required.
- Ability to formulate trading, execution, liquidity, and portfolio implementation questions as testable hypotheses using statistics, algebra, mathematical reasoning, and data-driven inference is required.
- Hands-on experience building, validating, and interpreting predictive models, transaction cost models, optimization frameworks, machine learning models, or artificial intelligence-assisted research workflows used in trading or execution analytics is required.
- Experience analyzing real-world trading datasets, including orders, executions, quotes, dealer responses, benchmarks, prices, liquidity signals, and portfolio attributes, is required.
- Expert-level Python skills for quantitative research, modeling, data engineering, and production-quality analytical development are required, including object-oriented programming and reusable, maintainable code.
- Proficient SQL skills are required, including querying, joins, cardinality, duplicate handling, window functions, subqueries, and common table expressions.
- Fluency with pandas, NumPy, and scikit-learn is required.
- Experience using Git, reproducible research workflows, code review, testing, and documentation is required.
- Proficient knowledge in at least one of electronic trading, transaction cost analysis, or financial markets knowledge, particularly fixed income market structure, is required.
- Understanding of trading workflows, order lifecycle, request-for-quote protocols, dealer behavior, liquidity formation, best execution, and execution and pricing algorithms is required.
- 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 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.
- Analyze execution across Global Investment Grade Credit, Global High Yield Credit, Emerging Markets Debt, Securitized Credit, and Agency Mortgages.
- Track market structure, electronic trading, dealer behavior, request-for-quote protocols, and liquidity trends, and recommend enhancements to trading capabilities.
- Build research and analytical tools that help traders and portfolio managers evaluate execution strategies, dealer selection, liquidity conditions, and timing.
- 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 combining order management system, execution management system, 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.
- A master's or PhD in Statistics, Mathematics, Economics, Computer Science, Engineering, Finance, or a related quantitative discipline is preferred.
- Experience supporting institutional 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 is desirable.
- Experience with JIRA or business intelligence tools such as Tableau is preferred.
- Experience with application programming interfaces, Financial Information eXchange protocol, cloud data platforms, distributed data processing, or market and trading data infrastructure is beneficial.
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
$64.9B
Headquarters
Boston, Massachusetts
Founded
1933
Get referred to Wellington Management
See people who can refer or advise you
Simplify's Take
What believers are saying
- Clay chose Wellington to lead its August 31, 2026 $7 billion financing.
- WVB funds launched July 22, 2026 on Bank of America wealth platforms.
- Wellington launched a market-neutral global equity UCITS fund on April 13, 2026.
What critics are saying
- Wellington cut 170 jobs, over 5%, after a 2025 strategic review.
- Hartford Funds integration risks distraction until the expected first-quarter 2027 close.
- Passive giants and private-market rivals squeeze fees, threatening Wellington's $1.35 trillion franchise.
What makes Wellington Management unique
- Wellington's $1.35 trillion platform spans public markets, private credit, and wealth distribution.
- Its alliance with Vanguard and Blackstone gives unique access to retail private-market packaging.
- Hartford Funds acquisition folds U.S. wealth into Wellington's brand by Q1 2027.
Help us improve and share your feedback! Did you find this helpful?
Benefits
Comprehensive health coverage
Work-life balance
Financial future
Development
Company News
Clay, a New York-based startup selling AI tools for sales and marketing teams, has agreed to a new funding round led by Wellington Management at a $7 billion pre-money valuation, Axios reported on 31 August. The round's exact size has not been disclosed, and it is unclear whether it has fully closed. The valuation marks Clay's third jump in about a year, up from $3.1 billion in August 2025 and $5 billion in a January 2026 employee tender offer led by DST Global. The company was founded in 2017 by Kareem Amin and Nicolae Rusan. Clay markets itself as a go-to-market platform that pulls data from over 150 external sources and uses AI agents to research prospects and personalise outbound sales messages. Its customers reportedly include OpenAI, Anthropic, and Canva.
Muon Space closed a $250M Series C led by Eclipse Capital and joined by Google & Salesforce Ventures to scale constellation production....
Avere Therapeutics announced a $500 million private placement to advance AVR-001, its once-weekly oral IL-23 receptor antagonist for inflammatory diseases. Leading healthcare investors including Venrock Healthcare Capital Partners, General Atlantic, Blackstone Multi-Asset Investing, and Wellington Management participated in the funding round. Combined with a previously announced $320 million concurrent private investment, the proceeds will fully fund Avere's operations through 2029. The financing will close alongside Avere's merger with NextCure, expected in the second half of 2026. Upon completion, the combined company will operate as Avere Therapeutics and trade on Nasdaq under ticker symbol "AVRX." Avere is initially developing AVR-001 for psoriasis, with potential expansion into ulcerative colitis, Crohn's disease, and psoriatic arthritis.
The $545 million Series G nearly doubles Whatnot's valuation—a rare consumer-marketplace breakout at a moment when nearly every venture dollar is flowing to AI.
Tarsus Pharmaceuticals has secured $125 million through an oversubscribed private placement equity financing. The transaction includes participation from investors in Alkeus Pharmaceuticals, which Tarsus announced it would acquire earlier the same day. Notable investors include TCGX, Bain Capital Life Sciences, Wellington Management, ADAR1 Capital Management, Sirenia Capital Management LP, RTW Investments, and Vestal Point Capital. The company is selling 2,098,519 shares of common stock at $56 per share and pre-funded warrants to purchase 133,625 additional shares. The financing is expected to close on 7 August 2026. Tarsus intends to use the proceeds to fund clinical development, commercial activities, and general corporate purposes. Barclays is serving as lead placement agent, with BofA Securities and William Blair as co-placement agents.