Top Quant Finance Jobs

Develop and improve quantitative equity investment strategies across the research lifecycle, including idea generation, complex data sourcing, statistical and machine-learning modeling, signal testing, portfolio construction, optimization, risk modeling, and trade execution. Present research findings, implement results, and collaborate with fixed-income and macro research teams.

Lead development of wholesale credit risk models supporting CECL, CCAR, client ratings, and regulatory risk management. Build scalable quantitative methods, high-performance computing solutions, analytics frameworks, and modeling pipelines using large datasets and machine learning. Assess model performance and risk, document methodologies, and communicate results and limitations to credit, finance, technology, governance, and regulatory stakeholders.

Design and implement production-scale quantitative risk models for derivatives clearing, including market risk, margin, volatility, correlation, stress testing, counterparty exposure, and automated liquidation. Validate models through backtesting and established frameworks, monitor production performance, document assumptions for audits, and collaborate with engineering, trading, and product teams. The role requires strong Python development, quantitative mathematics, derivatives expertise, and critical evaluation of AI-generated code and models.

Lead pricing analytics for Chase Auto, developing strategies and recommendations that balance volume growth and profitability. Partner with Finance Modeling to align assumptions and financial impacts, design and analyze pricing experiments, perform optimization and scenario analysis, and improve pricing frameworks. Translate statistical findings into concise reports and executive-ready recommendations for senior leaders.

Conduct quantitative research on financial markets, analyze large datasets, develop trading signals and models, design backtests, and help implement research ideas in live trading systems. The role involves mathematical and statistical analysis, programming, market microstructure research, strategy evaluation, and collaboration with quantitative researchers and engineers.

Develop and evaluate derivatives pricing models across equity and fixed income products; conduct alpha research, relative value analysis, backtesting, and signal implementation; build real-time P&L, risk, and analytics tools; assess model limitations; and advise portfolio managers and risk teams on practical model usage and business impact.

Leads quantitative research for systematic trading and analytics platforms. Develops predictive models, optimization techniques, trading infrastructure, and complex algorithms for equity and fixed-income markets. Assesses execution performance, supports production model development, coaches quantitative staff, and collaborates with technology, product, trading, and client-facing teams. The role also requires client engagement and consultation on market structure and trading strategies.

Conduct quantitative research for systematic securitized-credit trading strategies, focusing on agency MBS and CMOs. Responsibilities include developing trading signals, prepayment models, pricing and hedging analytics, researching new markets, improving systematic trading infrastructure, and supporting portfolio managers. The role requires applied statistics, programming, financial markets expertise, and communication skills, with AI tools used to improve research and trading workflows.

Analyze and manage Consumer & Community Banking interest rate risk and transfer pricing across deposits, cards, mortgages and loans. Maintain IRR models, perform scenario and sensitivity analysis, refine FTP methodologies, produce reports, and communicate recommendations to senior stakeholders. Leverage Python and statistical techniques, collaborate across teams, and support governance and regulatory requirements.

Graduate quant professionals analyze market data, develop trading strategies, build high-performance trading systems, improve data infrastructure, monitor live trades, and evaluate new markets. Depending on specialization, they may work as quantitative traders, researchers, or developers. The role requires strong quantitative reasoning, programming ability, financial-market interest, analytical problem-solving, communication, and attention to detail.

Make markets and manage risk across sports and financial prediction contracts. Develop real-time pricing models, integrate them into production trading systems, monitor and improve model performance, identify market-taking opportunities, and implement automated trading strategies. Collaborate with product, engineering, quant, and compliance teams to launch new markets and contract types. Flexible working hours are required to align with global sporting schedules.

The Risk Analyst role requires conducting operational risk, credit risk, market risk analysis, creating models, and developing analytics with a quantitative background.

The role involves developing pricing and risk analysis tools, working closely with the trading desk, and addressing complex financial inquiries.

The Quant will optimize trading strategies, research and test potential alphas, and enhance the production trading system within a collaborative team.

Join the Equity Derivatives team to develop pricing models, conduct quantitative research, build Python/SQL tools, design and run backtests, support traders on risk and trade analysis, and integrate models with trading and risk infrastructure using statistical and ML techniques.

Lead development of advanced equity derivatives risk analytics (volatility surface calibration, vanilla and exotic option pricing, VaR, stress testing). Partner with trading, risk, and quant teams to architect scalable pricing and risk engines, advise senior stakeholders on exposures and model assumptions, and drive methodological innovation for complex equity structures.

Join MerQube's New York team to model, backtest, and launch indices and options-based strategies. Develop implied volatility and pricing frameworks for options and exotic derivatives, translate models into production-ready Python indices, collaborate with financial engineering and clients, use datasets like FactSet and LSEG Refinitiv, and contribute to thought leadership.