As a Quantitative Research Analyst, you will work at the intersection of quantitative finance, data, and machine learning. You will design, prototype, and productionize models — both machine learning and classical statistical/financial — that power the research delivered to our subscribers. You’ll partner with senior quantitative analysts to take ideas from hypothesis to backtest to publication-ready research.
Responsibilities
- Understand the theory and practical implementation of quantitative finance and data science techniques.
- Design and implement new quantitative models — balanced across machine learning and classical statistical/econometric/financial models — to support research products.
- Evaluate, backtest, and iterate on new and existing models.
- Build and maintain reproducible research data pipelines that query, stage, and transform data from PostgreSQL and other internal and external sources (vendor APIs, flat files, cloud storage).
- Conduct ad-hoc research and produce written and visual analyses to communicate findings to senior analysts and stakeholders.
- Work closely with quantitative researchers, engineers, and product managers to understand requirements and enhance data-driven systems.
- Document and hand off research artifacts (models, signals, datasets) to engineering for production integration.
- Optimize research code and data workflows for speed and reproducibility.
- Proactively address data quality issues and implement solutions to ensure data accuracy and reliability.
The ideal candidate will be an analytical and technical problem solver, curious about financial markets and quantitative finance, and able to work autonomously and as part of a distributed team.
Qualifications
Required
- Bachelor’s degree in Computer Science, Mathematics, Quantitative Finance, Physics, Engineering, Financial Engineering, Statistics, or a similar discipline.
- 3–5 years of relevant work experience in quantitative finance, data analytics and modeling, or related fields.
- Hands-on experience building, validating, and deploying both machine learning models (e.g., scikit-learn, XGBoost, PyTorch or TensorFlow) and classical statistical/econometric models (e.g., statsmodels, time-series methods), with the judgment to choose the right tool for the problem.
- Strong programming skills in Python, including pandas, NumPy, and the scientific stack; comfort writing modular, testable code and using Git.
- Proficiency with SQL and relational databases (PostgreSQL preferred), including complex joins, window functions, and query performance tuning. Experience designing or contributing to data staging and transformation workflows.
- Experience working with financial time-series data and an understanding of common pitfalls (look-ahead bias, survivorship bias, regime changes).
- Familiarity with backtesting frameworks and standard performance/risk metrics (Sharpe, drawdown, turnover, etc.).
- Familiarity with quantitative finance concepts and financial instruments.
- Working knowledge of financial statements and fundamental data sufficient to incorporate them as model features.
- Excellent communication skills and the ability to collaborate effectively within a dynamic team.
Nice to Have
- Exposure to cloud data infrastructure on AWS.
- Strong academic or professional background in quantitative finance.
What We Offer
- Be part of a creative, fast-paced team that produces high-profile financial content.
- An entrepreneurial and innovative environment
- Competitive salary at $150,000, commensurate with experience.
- Comprehensive benefits package including health, dental, and vision insurance, 401 (k) match, and 12 paid company holidays.