Top Quantitative Researcher Jobs

As a Quantitative Researcher Intern, you'll develop models and strategies, conduct statistical analysis, and implement algorithms in a trading context.

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.

As a PhD intern, you will develop and test trading strategies, perform statistical analysis, and collaborate with senior researchers.

Conduct quantitative research for automated trading strategies by developing mathematical models, translating algorithms into code, backtesting trading signals, analyzing unconventional data, and refining monetization systems. Collaborate with senior researchers during an 11-week post-doctoral internship while applying advanced probability, statistics, machine learning, and time-series techniques to financial markets.

Conduct quantitative investment research by building predictive models, applying statistical and mathematical techniques, optimizing multi-asset portfolios, and developing optimization algorithms. Analyze financial markets, price formation, and risk using large-scale data and coding in C++ or Python. Collaborate with researchers, engineers, and traders to develop scalable systems and identify investment opportunities.

Develop systematic quantitative signals and alpha models focused on ETF and index trading inefficiencies. Gather, manage, and analyze financial and economic data, including market microstructure datasets, and support portfolio construction and strategy implementation. Collaborate with researchers, portfolio managers, engineers, and trading teams to implement new strategies. The role also involves managing research projects end to end and mentoring junior researchers.

Conduct quantitative research using the scientific method to develop investment models, analyze large datasets with machine learning, test investment ideas, and collaborate with engineers. The 10-week summer internship includes mentorship, academic engagement, a dedicated project, and a final presentation. Candidates should have quantitative technical training, programming experience, real-world research experience, strong analytical thinking, and clear communication skills.

Research and develop execution models, trading tactics, market impact models, simulators, scheduling methods, and short-horizon signals. Analyze order book, quote, trade, order, and fill data to improve execution costs and capacity. Design production experiments, evaluate execution metrics, and deploy research into trading systems while collaborating with portfolio management, modeling, engineering, and trading teams.

Conduct quantitative research for macro and fixed-income markets by developing alpha-generating investment models. Responsibilities include idea generation, feature development, alpha forecasting, monetization, portfolio construction, portfolio management, dataset analysis, and applying artificial intelligence and machine learning techniques. The role involves collaborating with quantitative researchers and developers to solve complex investment problems and communicate findings clearly.

Develop sophisticated investment and trading models using statistical techniques, machine learning, and real-world datasets. Conduct end-to-end quantitative research, test investment hypotheses, collaborate with engineers, analyze market behavior, and communicate complex findings. Participate in academic seminars, reading groups, and industry conferences focused on statistics, machine learning, and data science.

Develop and test sophisticated investment models using scientific methods, machine learning, quantitative techniques, and large datasets. Conduct independent research, create investment ideas, collaborate with engineers, analyze real-world data, and communicate complex findings. Participate in research discussions, academic seminars, and industry conferences covering statistics, machine learning, and data science.

Develop sophisticated quantitative trading models using scientific methods, machine learning, statistical techniques, and large datasets. The researcher will create and test investment ideas, collaborate with engineers, follow current academic research, attend seminars and conferences, and share insights in statistics, machine learning, and data science.

Develop and deploy advanced statistical and ML models on large-scale alternative datasets to generate investable signals, partner with investment teams, evaluate data quality, and drive novel data-driven research with commercial impact.

Conduct quantitative research for investment strategies using advanced statistical and mathematical modeling, machine learning, data analysis, portfolio optimization, and algorithm development. Build predictive models and analyze price formation, risk, and financial market drivers. Collaborate with researchers, engineers, and traders to develop scalable systems and solve real-world investment problems.

Conduct quantitative research for proprietary trading strategies by analyzing high-frequency market data, developing statistical and machine-learning models, building simulations and backtesting tools, and evaluating model performance. Collaborate with traders and developers to validate algorithms, improve pricing and execution, assess production readiness, prioritize research initiatives, and integrate validated models into low-latency trading systems. Mentor graduate researchers and interns.

Develop and improve mathematical models and trading approaches. Conduct statistical analysis and back test trading strategies in a fast-paced environment.

Conduct innovative machine learning research and develop scalable, robust models for financial markets across asset classes. Apply deep learning, sequence and time-series modeling, natural language processing, large language models, pre-training, fine-tuning, and reinforcement learning to model price formation, market behavior, and risk. Design, implement, optimize, and deploy models using Python and modern machine learning frameworks while collaborating with quantitative researchers, engineers, and traders.

Conduct quantitative research for financial markets by developing valuation strategies, mathematical models, trading signals, and monetization systems. Responsibilities include translating algorithms into code, backtesting and implementing trading models, analyzing unconventional data sources, and applying statistical techniques. The 11-week post-doctoral internship involves collaboration with senior researchers in a fast-paced investment environment.

Research and develop systematic US equities trading strategies across intraday and medium-frequency horizons. Responsibilities include alpha discovery, statistical modeling, backtesting, validation, portfolio construction, risk management, execution, live deployment, performance monitoring, and optimization. The researcher will own the full research-to-production lifecycle, collaborate with trading and engineering teams, and contribute directly to strategy performance and P&L.

Conduct quantitative research for algorithmic trading and market-making strategies. Design, test, deploy, and monitor models supporting pricing, risk, and execution. Build research and backtesting tools, analyze large datasets and model outputs, and collaborate with traders, developers, and senior researchers to translate hypotheses into production trading decisions. Continuously improve strategies based on market dynamics, data drift, and performance results.

Research and develop high- to mid-frequency trading strategies using market data, machine learning, and market microstructure analysis. Build exchange simulators, alpha-generation tools, post-trade monitoring systems, and performance analyses for ultra-low-latency strategies. The role requires designing, implementing, calibrating, and deploying quantitative trading strategies while analyzing latency, profitability, and competitiveness. Employees must work onsite at least three days per week.

Conduct quantitative research on trading signals, alpha, features, market microstructure, execution quality, and options pricing. Design, backtest, validate, and deploy models for automated trading strategies. Collaborate with traders and engineers to implement research in production, use AI tools to accelerate analysis, and contribute to data pipelines, evaluation frameworks, and reproducibility infrastructure.

Conduct quantitative research on fixed income and macroeconomic markets, developing portfolio allocation, optimization, risk, scenario-analysis, and performance-attribution models. Translate research into actionable investment frameworks and production tools using Python, SQL, and quantitative libraries. Partner with portfolio managers, traders, economists, and engineers, lead research projects, support new strategy launches, mentor colleagues, and communicate findings to investment professionals and clients.

Develop and optimize quantitative trading strategies using large-scale financial datasets, statistical modeling, machine learning, and data analysis. Responsibilities span signal development, backtesting, production implementation, performance monitoring, and optimization. The role requires writing scalable code for live trading systems, diagnosing strategy behavior, collaborating with researchers, traders, and engineers, and continuously improving prediction and execution performance.

Conduct quantitative and statistical analysis of private markets and investment opportunities using proprietary datasets and models. Lead data exploration, visualization, risk-return analysis, investment diligence, portfolio insights, and integration of quantitative models with fundamental deal-team research. Apply AI and machine learning techniques, communicate actionable findings, and support client-facing teams with ad hoc analysis. The role requires strong quantitative research, programming, statistical modeling, and large-dataset analysis experience.