Quantitative Researcher / Developer (Python) – Mid Level

We are seeking a Quantitative Researcher / Developer with strong Python skills and practical experience in factor-based investing to help design, test, and implement systematic trading strategies. You will work closely with a fundamental analyst to turn investment insights into robust, scalable models and backtesting frameworks.

Key Responsibilities

Factor Research & Development

• Translate fundamental and alternative data into quantifiable factors.

• Clean, standardize, and normalize financial datasets for analysis.

• Build and test composite scoring models across value, quality, momentum, and other styles.

Backtesting & Validation

• Develop and maintain backtesting frameworks with realistic assumptions (transaction costs, slippage, liquidity).

• Run decile/quantile portfolio tests and analyze long-short spreads.

• Perform robustness checks: sub-period tests, out-of-sample validation, stress testing.

Portfolio Construction & Risk Management

• Implement systematic long-short and long-only strategies with exposure and risk constraints.

• Monitor factor correlations, portfolio turnover, and drawdowns.

• Evaluate strategies using Sharpe, information ratio, and other performance metrics.

Implementation & Automation

• Automate research pipelines for data ingestion and factor updates.

• Assist in connecting models to broker APIs for semi-automated or automated execution.

• Build dashboards and reporting tools to monitor live strategy performance.

Qualifications

Technical Skills

• Strong proficiency in Python (pandas, numpy, matplotlib, statsmodels, scikit-learn).

• Experience working with financial time series and backtesting strategies.

• Solid grounding in statistics, econometrics, and portfolio theory.

• Familiarity with SQL or similar databases; experience with version control (Git).

Finance & Markets Knowledge

• Understanding of equity markets and factor investing concepts (value, momentum, quality, etc.).

• Prior exposure to multi-factor portfolio construction and performance attribution.

• Knowledge of transaction cost modeling and basic market microstructure is a plus.

Soft Skills

• Ability to collaborate with fundamental analysts and other researchers.

• Clear communicator who can present findings with rigor and transparency.

• Curious, detail-oriented, and comfortable iterating quickly.

Nice-to-Have

• Hands-on experience with broker APIs (Interactive Brokers, QuantConnect, Alpaca).

• Exposure to cloud computing, distributed backtesting, or alternative data integration.

• Familiarity with ML techniques for feature selection and nonlinear factor modeling.