Quantitative Research Scientist

Bridge Et Al.•American Fork, UT

5d•$6,000 - $8,000•Onsite

About The Position

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.

Requirements

Master's or PhD in Mathematics, Data Science, Computer Science, or a related quantitative field.
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 NumPy, Pandas, SciPy, 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.

Responsibilities

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.