Quantitative Research Scientist

Location: Washington, DC, Florida, or Texas

Employment Type: Contract (3 months)

Industry: Investment Management

We are seeking a highly analytical and mathematically driven
Quantitative Research Scientist
to join a leading investment management firm. The role focuses on developing a Market Risk Indicator (MRI) framework to support quantitative investment research and systematic investment strategies.

• Research, understand, and implement the Log Periodic Power Law (LPPL) framework.

• Develop mathematical models to identify market bubbles, regime shifts, and potential market turning points.

• Extend the methodology to analyse multiple asset classes, including equities, bonds, commodities, currencies, cryptocurrencies, and macroeconomic indicators.

• Research and evaluate alternative market risk methodologies, including Turbulence Index models and other quantitative risk indicators.

• Apply Artificial Intelligence and Machine Learning techniques to automate LPPL parameter estimation.

• Improve model calibration, optimisation, and prediction accuracy using modern data science methodologies.

• Explore innovative approaches for identifying financial anomalies and super-exponential growth patterns.

• Develop clean, scalable, and reusable analytical code primarily in Python.

• Build flexible tools capable of analysing individual assets or multiple assets across custom and predefined time windows.

• Ensure outputs can be integrated seamlessly into the firm's internal dashboards and research infrastructure.

• Maintain documentation for models, assumptions, methodologies, and code.

• Analyse large financial and economic time-series datasets.

• Interpret model outputs and communicate research findings to investment professionals.

• Support continuous improvement of quantitative research methodologies and contribute to future research initiatives.

Master's or PhD in one of the following disciplines:

• Mathematics

• Data Science

• Computer Science

• 1–2 years of research or industry experience in quantitative modelling, data science, machine learning, or financial analytics.

• Strong mathematical, statistical, and analytical problem-solving skills.

• Experience in time-series analysis, optimisation, numerical methods, and statistical modelling.

• Proficiency in Python and scientific computing libraries such as Num Py, Pandas, Sci Py, and scikit-learn.

• Working knowledge of Machine Learning and Artificial Intelligence techniques.

• Ability to independently understand and implement complex mathematical research with minimal supervision.

• Excellent programming skills with experience developing modular and maintainable code.

USD 6k-8k per month.