Top Quantitative Researcher Jobs

Leads quantitative research for private-market investment decisions and portfolio management. Develops statistical models, analyzes complex datasets, evaluates investment opportunities, communicates actionable insights, and leads research initiatives with investment leadership. Supports fundraising and client-facing research while incorporating AI and machine-learning techniques. Requires strong quantitative investment expertise, advanced Python and SQL skills, and the ability to influence technical and investment stakeholders.

Conduct quantitative research for high-frequency trading and prediction markets. Analyze order book, trade, sports, and prediction-market data; develop statistically significant signals, pricing models, liquidity-management techniques, and latency-sensitive trading strategies. Interns may rotate across trading tracks and participate in coding tests, quantitative interviews, workshops, and training.

Lead end-to-end quantitative market research, including research planning, surveys, advanced statistical analysis, synthesis, and stakeholder presentations. Build and manage brand and sentiment measurement programs, identify customer and market trends, and provide strategic recommendations for growth and product opportunities. Partner with business leaders, promote research-driven decision-making, and mentor junior researchers while scaling the organization’s research practice.

Conduct quantitative research for systematic FX options and volatility strategies. Develop forecasting models, trading signals, market-making approaches, and systematic hedging strategies. Own the full research lifecycle, including data analysis, feature engineering, backtesting, validation, portfolio construction, performance attribution, stress testing, and live deployment. Collaborate closely with traders and engineers, build analytical tools, monitor production performance, and communicate research findings, assumptions, and risks to senior stakeholders.

Conduct quantitative research on market behavior and trading strategies. Responsibilities include discovering predictive patterns, designing statistical and machine-learning models, building AI research agents, developing large-scale research infrastructure, running experiments and backtests, and translating findings into production-ready trading signals. Interns complete an end-to-end research project while collaborating with researchers and technologists.

Conduct quantitative research for systematic trading by analyzing market data, formulating and testing hypotheses, developing statistical and machine learning models, backtesting strategies, studying market microstructure, and partnering with technologists to deploy production-ready research and trading systems.

Support portfolio managers with quantitative analysis, portfolio monitoring, and risk evaluation. Build and maintain analytical tools, analyze large datasets for investment insights, assist with systematic fixed income strategies, performance attribution, and reporting in collaboration with research and technology teams.

Conduct quantitative research to design, implement, and deploy mathematical trading models. Build custom tools for exploratory analysis, apply rigorous quantitative techniques to solve complex problems, and collaborate with traders and developers to productionize models while maintaining best practices.

Leads quantitative research across credit and equity markets by designing systematic strategies, building statistical and mathematical models, analyzing complex financial datasets, and translating findings into explainable signals. Collaborates with engineering and product teams to deploy models, improve factor libraries, support institutional research workflows, author research notes, and shape the company’s research roadmap.

Design, research, deploy, and maintain statistical and machine learning models that detect malicious behavior in real time. Analyze unconventional data and long user-activity histories, distinguish hostile activity from normal behavior, prepare datasets and model features, and optimize models for accuracy and speed. Own research projects through production while collaborating with researchers and engineers.

Conduct quantitative and machine learning research for real-time account-compromise detection. Design, train, evaluate, deploy, and scale production models while working with customer attack data. Build systems for streaming pipelines, feature stores, high-performance databases, and distributed infrastructure. Contribute production-grade code, establish technical standards, and advance detection capabilities in a high-autonomy cybersecurity startup environment.

Design, train, evaluate, and deploy machine learning detection models that identify account compromise in real time. Move research models into scalable production systems, work with real-time data and distributed infrastructure, and architect solutions for rapid growth. Engage with customers to understand emerging attacks, shape technical direction, establish engineering standards, and raise the quality of a small, autonomous research and engineering team.

Leads complex CMS Innovation Center evaluations involving Medicare and Medicaid payment models, quasi-experimental and mixed-methods research, claims and EHR analysis, and beneficiary and provider outcomes. Supports federal business development and proposal efforts, manages tasks and workstreams, maintains client relationships, coaches staff, and communicates findings to technical and non-technical audiences.

Build data foundations and end-to-end pipelines, research and implement quantitative pricing, market-making, and risk models for prediction markets, model cross-market dependencies and parlays, develop backtesting/simulation frameworks, monitor model performance and collaborate closely with traders to improve pricing and trading outcomes.

Design and deploy quantitative market-making and systematic trading models across FICC products using statistical methods and advanced machine learning. Build calibration frameworks, risk management and portfolio optimization tools, work with large time-series data, and collaborate with traders, quant developers, and engineering to integrate pricing, trading, and risk systems.

Build environments, agents, evaluations, reward systems, and post-training methods for quantitative research and systematic decision-making. Develop realistic market and portfolio simulations, train long-horizon research agents, assess out-of-sample robustness, and create production feedback loops with monitoring and failure attribution. Collaborate directly with customers and research partners while maintaining a substantive research agenda.

Conduct applied quantitative research in global financial markets by analyzing large-scale market and order-flow data, developing predictive, statistical, stochastic, and machine learning models, testing hypotheses, and identifying trading opportunities. Researchers validate models in live market environments and collaborate with engineers and traders. The role includes structured graduate onboarding and requires a PhD in a quantitative STEM field, expected completion by mid-2027.

The Quantitative Strategist will develop predictive models, analyze trading strategies, build risk frameworks, and automate research tasks within a collaborative team environment.

Develop and implement statistical and machine learning trading strategies and quantitative models. Design optimization algorithms for portfolio construction and explore new research opportunities within derivatives and market behavior.

Conduct quantitative research to identify trading opportunities and develop trading strategies. Analyze financial datasets using statistical and probabilistic methods, distinguish signal from noise, and investigate market dynamics and microstructure. Build and use research frameworks, apply coding skills in languages such as C++ and Python, and collaborate across projects to improve the firm’s trading capabilities and profitability.

Develop fundamental supply-and-demand models for global crude oil markets using econometric and statistical methods. Build Python and SQL analytics, visualization tools, data validation frameworks, and automated ingestion pipelines. Analyze market dislocations, disruptions, policy changes, and data releases to formulate probabilistic trade recommendations. Curate and transform proprietary commodity datasets, backtest signals, and present trade ideas to senior traders and portfolio managers.

Conduct research and develop automated trading strategies using advanced statistical techniques and programming skills. Collaborate in a fast-paced team to innovate and implement quantitative models.

Curate, cleanse, validate, and maintain large US equity and options datasets; integrate fundamental and alternative data; build data pipelines for research and trading; perform statistical and ML analysis to identify market patterns; document data provenance and collaborate with engineers and traders to deploy production-grade models.

Conduct quantitative research to improve and design trading models, build tooling, implement and deploy models to production, and collaborate with traders and developers to solve complex problems.

Develop and maintain portfolio construction factor models, research statistical market patterns, create valuation strategies, and backtest financial signals for live trading. The role applies machine learning, statistics, and quantitative finance to investment products while collaborating with investment leadership, Product, Engineering, Compliance, and Legal on launches, methodology improvements, and policies.