Develop and evaluate quantitative equity investment models and trading signals using statistical and machine learning techniques. Engineer features from large financial datasets, conduct empirical and cross-sectional research, and build scalable Python research pipelines using distributed computing and cloud infrastructure. Evaluate signal quality and portfolio performance through backtesting, risk analysis, attribution, and transaction-cost modeling. Collaborate with portfolio managers, domain experts, and engineers to implement investment ideas in production systems.
Position Summary
Job Location: 100 Avenue of the Americas, New York, NY 10013
Note: Company "Hybrid" work attendance policy: In-office work attendance required at the aforementioned office address for collaboration days based on each team's requirement; telecommuting / working from home is permissible for remainder of the same month.
Duties: Develop and evaluate quantitative equity investment models using statistical and machine learning techniques including linear models, regularization methods, tree-based models, and neural networks to generate predictive trading signals across multiple return horizons. Collaborate with portfolio managers and domain experts to formulate investment hypotheses and alpha ideas, translating market intuitions into testable quantitative signals. Engineer features from diverse large-scale data sources including equity fundamentals, analyst forecasts, corporate earnings events, and market microstructure data across intraday and overnight trading sessions. Construct and evaluate alpha signals including event-driven, composite, and cross-sectionally propagated signal strategies, performing post-processing such as risk factor residualization and decorrelation against existing signals. Analyze cross-sectional and time-series relationships across market segments, identifying variation in signal strength by market capitalization, liquidity, volatility, and industry classification. Perform exploratory data analysis and statistical testing to validate investment hypotheses, characterize information flow dynamics, and assess predictive content of novel data sources. Design and implement end-to-end scalable research pipelines in Python for distributed data processing, parallel model training, and automated experiment management using cloud computing infrastructure. Process and analyze very large financial datasets using distributed computing frameworks, optimizing data loading, storage, and computation for efficient research iteration. Evaluate signal quality and portfolio performance through information coefficient analysis, Sharpe ratio estimation, return attribution across market segments, and optimizer-based portfolio simulation with transaction cost modeling. Collaborate with engineers to implement and test quantitative investment ideas in production-scale systems and shared research frameworks.
Minimum education and experience required: Master's degree or the equivalent in Mathematics, Computer Science, Statistics, Finance or related field; OR Bachelor's degree or the equivalent in Mathematics, Computer Science, Statistics, Finance or related field plus three years of experience in quantitative analysis or related experience.
*Skills required: Must have experience with developing quantitative trading strategies, including feature engineering, signal construction, backtesting & portfolio simulation. Must have experience with predictive modeling techniques including linear models, regularization methods, tree-based models, neural networks & ensemble methods. Must have experience with large-scale data processing & distributed computing using cloud infrastructure for parallelized data loading, model training & simulation. Must have experience conducting empirical research, including formulating hypotheses, structuring experiments, diagnosing confounding variables & iterating on results. Must have experience performing financial data analysis & alpha research on fundamental, technical & microstructure data. Must have experience with scientific programming using data manipulation, machine learning & visualization libraries. Must have experience with engineering practices including version control, code review & scalable research pipeline design. Must have experience with quantitative analysis methods including cross-sectional and time-series analysis & hypothesis testing. Must have experience with performance attribution, including decomposing model outputs across market segments, time periods & regime to identify sources of alpha, diagnose degradation & guide iterative model improvement. Must pass company's required skills assessment. Employer will accept any amount of experience with the required skills.
The base pay for this role will be between $165,000 and $325,000 per year. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.
#LI-DNI
Job Location: 100 Avenue of the Americas, New York, NY 10013
Note: Company "Hybrid" work attendance policy: In-office work attendance required at the aforementioned office address for collaboration days based on each team's requirement; telecommuting / working from home is permissible for remainder of the same month.
Duties: Develop and evaluate quantitative equity investment models using statistical and machine learning techniques including linear models, regularization methods, tree-based models, and neural networks to generate predictive trading signals across multiple return horizons. Collaborate with portfolio managers and domain experts to formulate investment hypotheses and alpha ideas, translating market intuitions into testable quantitative signals. Engineer features from diverse large-scale data sources including equity fundamentals, analyst forecasts, corporate earnings events, and market microstructure data across intraday and overnight trading sessions. Construct and evaluate alpha signals including event-driven, composite, and cross-sectionally propagated signal strategies, performing post-processing such as risk factor residualization and decorrelation against existing signals. Analyze cross-sectional and time-series relationships across market segments, identifying variation in signal strength by market capitalization, liquidity, volatility, and industry classification. Perform exploratory data analysis and statistical testing to validate investment hypotheses, characterize information flow dynamics, and assess predictive content of novel data sources. Design and implement end-to-end scalable research pipelines in Python for distributed data processing, parallel model training, and automated experiment management using cloud computing infrastructure. Process and analyze very large financial datasets using distributed computing frameworks, optimizing data loading, storage, and computation for efficient research iteration. Evaluate signal quality and portfolio performance through information coefficient analysis, Sharpe ratio estimation, return attribution across market segments, and optimizer-based portfolio simulation with transaction cost modeling. Collaborate with engineers to implement and test quantitative investment ideas in production-scale systems and shared research frameworks.
Minimum education and experience required: Master's degree or the equivalent in Mathematics, Computer Science, Statistics, Finance or related field; OR Bachelor's degree or the equivalent in Mathematics, Computer Science, Statistics, Finance or related field plus three years of experience in quantitative analysis or related experience.
*Skills required: Must have experience with developing quantitative trading strategies, including feature engineering, signal construction, backtesting & portfolio simulation. Must have experience with predictive modeling techniques including linear models, regularization methods, tree-based models, neural networks & ensemble methods. Must have experience with large-scale data processing & distributed computing using cloud infrastructure for parallelized data loading, model training & simulation. Must have experience conducting empirical research, including formulating hypotheses, structuring experiments, diagnosing confounding variables & iterating on results. Must have experience performing financial data analysis & alpha research on fundamental, technical & microstructure data. Must have experience with scientific programming using data manipulation, machine learning & visualization libraries. Must have experience with engineering practices including version control, code review & scalable research pipeline design. Must have experience with quantitative analysis methods including cross-sectional and time-series analysis & hypothesis testing. Must have experience with performance attribution, including decomposing model outputs across market segments, time periods & regime to identify sources of alpha, diagnose degradation & guide iterative model improvement. Must pass company's required skills assessment. Employer will accept any amount of experience with the required skills.
The base pay for this role will be between $165,000 and $325,000 per year. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.
#LI-DNI
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