Remote Quantitative Analyst Work From Home

About this role

We are looking for a Quantitative Analyst to join our team and turn data into useful insights. You will work with large datasets, create quantitative models, test strategies, and collaborate with analysts, researchers, and technology experts. This is a great opportunity for someone who enjoys using math, statistics, programming, and financial research in a flexible remote setting. What You'll Do: - Develop and improve quantitative models for forecasting, pricing, risk, and investment analysis.

- Analyze large financial and alternative datasets to find meaningful patterns and opportunities. - Build and maintain backtesting frameworks to evaluate quantitative strategies. - Perform statistical analysis, hypothesis testing, and time-series modeling. - Use Python and SQL to clean, analyze, and transform data. - Apply machine learning techniques where appropriate. - Monitor how models perform and investigate unexpected results.

- Document your research, assumptions, methods, and results. - Present your findings clearly to both technical and non-technical colleagues. - Collaborate with research, engineering, investment, and risk teams. - Continuously explore new quantitative techniques, datasets, and technologies. What We're Looking For: We welcome candidates with experience or strong academic/project work in quantitative analysis. Required: - Strong analytical and problem-solving skills.

- Proficiency in Python. - Working knowledge of SQL. - Understanding of probability, statistics, and quantitative modeling. - Familiarity with regression, time-series analysis, or machine learning. - Ability to work independently in a remote environment. - Strong written and verbal communication skills. - Bachelor's, Master's, or PhD in Mathematics, Statistics, Economics, Finance, Computer Science, Engineering, Physics, or a related field — or equivalent practical experience.

Nice to Have: - Experience in quantitative finance, trading, portfolio analytics, risk, or pricing. - Experience building or evaluating backtests. - Knowledge of financial markets and asset classes. - Experience with tools like NumPy, Pandas, SciPy, scikit-learn, or statsmodels. - Experience with R, C++, MATLAB, or similar languages. - Experience with large datasets or cloud platforms. - Knowledge of factor models, portfolio optimization, derivatives, or market dynamics.