Quantitative Research Analyst (The Vanguard Group, Inc. / Malvern, PA) -- Provide thought leadership and advocacy to identify and implement new initiatives that improve fixed income fund returns and lower their costs; work independently on research projects under the guidance of senior specialists, to support the fixed income investment decision processes, utilizing specialized quantitative skills; generate value-add insights and provide data-driven solutions to enhance fixed income investment outcomes; investigate machine learning techniques and alternative data sources to introduce innovative techniques and methods in support of fixed income investment decision-making; independently create and perform analysis to support fixed income investment strategies and transaction costs, create new quantitative measures, and contribute to new investment strategies; enhance existing models, develop data sources and processes, and communicate results; develop business acumen to bring an informed perspective to investment management teams; use communication and interpersonal skills to integrate with portfolio management and trading teams, adding a quantitative perspective to their day-to-day interactions with and interpretations of market developments; tailor communication of complex concepts to audience. Requires Master's in Computational Finance, Mathematics, Computer Science, or another directly related quantitative field and two years of experience in job offered or in position(s) as Financial Engineer and/or App Developer - Java and/or Software Developer; or Bachelor's and five years of experience. Background in education, training or experience must include proficiency in programming languages including Python, R, MATLAB, or C++ for data manipulation, statistical analysis, and algorithm development; strong mathematical skills to create and manipulate quantitative models used in financial analysis and decision-making processes; knowledge of risk assessment techniques, including Value at Risk (VaR), stress testing, and scenario analysis to manage and mitigate financial risks effectively; proficiency in statistical methods, hypothesis testing, regression analysis, and time series analysis for interpreting complex data patterns; experience in data cleaning, preprocessing, and analysis using tools including Pandas, NumPy, or SQL databases.