Company Description Echo Financial Systems is an AI-native systematic equity research and institutional data platform designed for professional investors, researchers, and financial institutions. The platform transforms market, fundamental, technical, and macroeconomic data into structured analytics at multiple time horizons, enabling robust screening, portfolio research, model development, and bespoke analysis. Echo provides rich security-level research profiles, historical metric series, factor composites, and curated factor-interaction datasets that highlight transparent and explainable signals. The company focuses on turning raw financial information into commercially useful, research-ready data products. Echo’s long-term vision is to build a continuously expanding library of systematic research, proprietary rankings, and validated mathematical relationships across global markets.
Role Description The Founding Quantitative Researcher — Credit & Equity Modeling will lead the development of systematic models and research frameworks across credit and equity markets. This full-time hybrid role is based in the New York City or Boston Metropolitan Areas, with flexibility for partial work from home. Day-to-day responsibilities include designing and testing quantitative strategies, building and validating statistical and mathematical models, and translating market, fundamental, and macroeconomic data into robust, explainable signals. The person in this role will collaborate closely with engineering and product teams to implement models into production, refine factor libraries, and enhance research workflows for institutional clients. Additional responsibilities may include authoring systematic research notes, contributing to proprietary rankings, and helping shape Echo’s long-term research roadmap as an early foundational team member.
Qualifications
- Strong foundation in Statistics and Mathematics, including probability, econometrics, and applied linear algebra relevant to financial modeling.
- Experience in Quantitative Investing and Quantitative Research, particularly in credit and equity markets, with a track record of designing and evaluating systematic strategies.
- Advanced Analytical Skills for working with large, complex financial datasets, interpreting factor behavior, and diagnosing model performance.
- Proficiency with programming languages commonly used in quantitative finance (e.g., Python, R, or similar) and familiarity with data analysis libraries and numerical computing tools.
- Master’s or PhD in a quantitative discipline (e.g., Statistics, Mathematics, Finance, Computer Science, Physics, Economics) or equivalent practical experience.
- Experience building or using factor models, risk models, or signal libraries for institutional research or portfolio construction.
- Ability to communicate technical findings clearly to both quantitative and non-quantitative stakeholders, and to document research in a reproducible, structured manner.
- Comfort working in an early-stage environment, with the ability to take ownership, iterate quickly, and collaborate across research, product, and engineering teams.
Skills Required
- Strong foundation in statistics and mathematics, including probability, econometrics, and applied linear algebra for financial modeling.
- Experience in quantitative investing and quantitative research, particularly in credit and equity markets.
- Track record of designing and evaluating systematic strategies.
- Advanced analytical skills for working with large, complex financial datasets and diagnosing model performance.
- Proficiency with programming languages commonly used in quantitative finance, such as Python or R.
- Familiarity with data analysis libraries and numerical computing tools.
- Master’s or PhD in a quantitative discipline, or equivalent practical experience.
- Experience building or using factor models, risk models, or signal libraries for institutional research or portfolio construction.
- Ability to communicate technical findings clearly and document research reproducibly.
- Ability to take ownership, iterate quickly, and collaborate across research, product, and engineering teams in an early-stage environment.
