Quantitative Research Analyst

About the Role

This is a buy-side quantitative research role focused on identifying, validating, and maintaining market signals for a live investment intelligence product. You will determine what constitutes a genuine, tradable signal, apply rigorous statistical standards, and confidently defend the methodology and results to sophisticated financial clients.

What You Will Do

Own the Signal Set

• Evaluate and approve, hold, or deprecate candidate signals generated through the research process.
• Assess signal quality using metrics such as Information Coefficient (IC), AUC, hit rate, Precision@K, stability, and statistical significance.
• Ensure promoted signals pass out-of-sample validation and robustness tests, including placebo and permutation checks.
• Distinguish genuine predictive relationships, including inverse signals, from artifacts and implementation errors.

Backtesting & Research Design

• Own and enhance walk-forward testing methodologies, including purged cross-validation, embargo periods, and fold aggregation.
• Design and test multifactor research hypotheses involving sentiment, geography, volatility regimes, and other conditioning factors.
• Define and refine promotion thresholds based on evidence and false-discovery risk.
• Guard against data leakage, multiple testing bias, survivorship bias, and overfitting.

Prediction Quality & Calibration

• Monitor forecast performance using accuracy, calibration metrics, Brier scores, reliability curves, and market-specific analyses.
• Balance accuracy and coverage by defining defensible confidence thresholds.
• Establish abstention policies for low-confidence forecasts.
• Benchmark results against naïve baselines and ensure reported performance represents a genuine edge.

Monitoring & Signal Decay

• Track signal performance using rolling IC, statistical drift, and changepoint detection techniques.
• Oversee the resolution process that converts forecasts into realized outcomes and supports performance measurement.

Required Qualifications

• 5+ years in quantitative research, quantitative analysis, systematic investing, financial data science, or a related role within asset management, hedge funds, trading firms, banks, or financial data providers.
• Proven ownership of signal, factor, or predictive-model research.
• Strong understanding of financial markets, return forecasting, volatility regimes, and market dynamics.
• Deep knowledge of statistical testing, multiple-comparison corrections, rank correlation, ROC/AUC, calibration, and forecasting evaluation.
• Experience with time-series modeling, walk-forward validation, holdout design, and look-ahead bias prevention.
• Advanced Python skills (pandas, NumPy, SciPy, statsmodels, scikit-learn).
• Strong analytical SQL skills.
• Demonstrated intellectual rigor and a willingness to challenge and reject unsupported results.
• Strong written and verbal communication skills for both technical and non-technical audiences.

Preferred Qualifications

• Experience researching alternative data, news, sentiment, filings, or similar datasets.
• Familiarity with LightGBM, XGBoost, CatBoost, model stacking, and probability calibration techniques.
• Exposure to conformal prediction, selective prediction, abstention frameworks, or decision-threshold optimization.
• Experience supporting client-facing or commercially distributed data products.
• Familiarity with cloud analytics platforms such as BigQuery, Vertex AI, or equivalent.
• Graduate degree in Statistics, Econometrics, Financial Engineering, Mathematics, Physics, or a related quantitative discipline. CFA, CQF, or FRM is a plus.

Key Improvements

• Removed repetitive explanations and highly specific implementation details.
• Consolidated related responsibilities into broader, outcome-focused bullets.
• Shortened qualification descriptions while preserving technical rigor.
• Maintained buy-side quant research positioning and client-facing expectations.

About the Role

This is a buy-side quantitative research role focused on identifying, validating, and maintaining market signals for a live investment intelligence product. You will determine what constitutes a genuine, tradable signal, apply rigorous statistical standards, and confidently defend the methodology and results to sophisticated financial clients.

What You Will Do

Own the Signal Set

• Evaluate and approve, hold, or deprecate candidate signals generated through the research process.
• Assess signal quality using metrics such as Information Coefficient (IC), AUC, hit rate, Precision@K, stability, and statistical significance.
• Ensure promoted signals pass out-of-sample validation and robustness tests, including placebo and permutation checks.
• Distinguish genuine predictive relationships, including inverse signals, from artifacts and implementation errors.

Backtesting & Research Design

• Own and enhance walk-forward testing methodologies, including purged cross-validation, embargo periods, and fold aggregation.
• Design and test multifactor research hypotheses involving sentiment, geography, volatility regimes, and other conditioning factors.
• Define and refine promotion thresholds based on evidence and false-discovery risk.
• Guard against data leakage, multiple testing bias, survivorship bias, and overfitting.

Prediction Quality & Calibration

• Monitor forecast performance using accuracy, calibration metrics, Brier scores, reliability curves, and market-specific analyses.
• Balance accuracy and coverage by defining defensible confidence thresholds.
• Establish abstention policies for low-confidence forecasts.
• Benchmark results against naïve baselines and ensure reported performance represents a genuine edge.

Monitoring & Signal Decay

• Track signal performance using rolling IC, statistical drift, and changepoint detection techniques.
• Oversee the resolution process that converts forecasts into realized outcomes and supports performance measurement.

Required Qualifications

• 5+ years in quantitative research, quantitative analysis, systematic investing, financial data science, or a related role within asset management, hedge funds, trading firms, banks, or financial data providers.
• Proven ownership of signal, factor, or predictive-model research.
• Strong understanding of financial markets, return forecasting, volatility regimes, and market dynamics.
• Deep knowledge of statistical testing, multiple-comparison corrections, rank correlation, ROC/AUC, calibration, and forecasting evaluation.
• Experience with time-series modeling, walk-forward validation, holdout design, and look-ahead bias prevention.
• Advanced Python skills (pandas, NumPy, SciPy, statsmodels, scikit-learn).
• Strong analytical SQL skills.
• Demonstrated intellectual rigor and a willingness to challenge and reject unsupported results.
• Strong written and verbal communication skills for both technical and non-technical audiences.