Top Quant Finance Jobs

Lead quantitative model validation and governance for retail lending, CCAR, PPNR and forecasting. Evaluate conceptual soundness, assumptions, testing completeness, stress projections and overlays; measure model risk, maintain controls, support automation adoption, mentor juniors, and ensure regulatory compliance.

Lead model risk reviews for complex credit, interest rate, and equity pricing models. Assess model design, validation, performance metrics, and governance; develop benchmarks; guide model remediation; act as business liaison; and manage and mentor junior team members.

Support mortgage finance for the Global Private Bank by delivering profitability analysis, forecasting, stress-testing (CCAR) and CECL analytics, translating model outputs into executive insights, supporting model implementation/validation, and driving process improvements and automation across forecasting, credit, and prepayment risk.

Lead design, implementation, validation, and production readiness of regulatory VaR and related market-risk models for mortgage and securitized products. Develop pricing, prepayment/default, OAS, scenario, and sensitivity analytics; perform backtesting, benchmarking, and P&L attribution; translate regulatory/model-risk requirements into documentation, controls, and monitoring; partner with trading, risk, validation, technology, and senior leadership.

Manage and trade systematic index rebalancing and special-situation strategies. Build and backtest index prediction models and algo-trading systems using Python and KDB. Monitor program trade execution, perform portfolio construction and risk optimization, integrate performance analytics, produce research and trading commentary, and collaborate with trading, sales, and clients to forecast flows and manage risk.

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.

Lead quantitative model risk reviews for Treasury models used in resolution, recovery, liquidity, stress testing and budgeting. Assess conceptual soundness, design benchmarks and performance metrics, guide model usage, liaise with developers/users/compliance, and evaluate model performance regularly. Provide technical write-ups and escalate material issues.

Support development and maintenance of equity derivatives risk analytics: volatility calibration, option pricing, VaR, scenario and stress testing, sensitivity analysis, methodology enhancement, production issue resolution, and stakeholder documentation.

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.

Research, design, and backtest quantitative trading strategies across asset classes and frequencies; develop alpha, risk, and execution models; analyze large market datasets; evaluate DeFi protocols; collaborate with engineers to deploy and monitor live strategies; perform attribution and propose improvements.

Design and implement quantitative risk models and real-time monitoring for perpetuals and commodity derivatives. Build stress testing, default and guarantee-fund frameworks, translate model outputs into exchange policy (margin, liquidation, limits), and ensure CFTC reporting and capital compliance. Partner with engineers to deploy production-quality risk systems and prioritize risk architecture improvements as trading and exposure evolve.

Responsible for developing custom equity factors, reviewing exposures, creating risk management tools, and providing equity advisory services, with 7+ years of experience.

Perform quantitative analysis and build models/tools to inform equity long/short investment and business decisions. Analyze performance, risk attribution, and portfolio construction; run statistical tests; implement process improvements; apply AI tools to accelerate research; present findings to senior stakeholders and partner with PMs, trading, operations, technology, data, and research teams.

Design, validate, and refine quantitative methodologies for direct indexing, portfolio construction, tax-loss harvesting, and tracking-error minimization. Build backtests, analyze large financial datasets (market, execution, tax lots), improve optimization formulations, and collaborate with engineering and product teams to productionize robust, risk-aware investment models.

Support senior researchers to develop, implement, validate, and maintain option pricing models and quantitative analytics. Conduct statistical analysis on large financial datasets, contribute to new products and tools, document findings, and communicate technical results to technical and non-technical stakeholders in a hybrid NYC environment.

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.

The role involves developing models for ETF market making, backtesting trading strategies, and collaborating with the ETF trading desk on data and analytics.

Join a graduate quant researcher program focused on options and derivatives market making. Complete a 7-week London training, then perform two Chicago rotations building pricing models, improving automated trading algorithms, and researching trading data to identify and test opportunities. Program includes mentorship, training, and transition to a permanent role.

Design, test, and deploy systematic trading strategies across digital asset markets. Generate and validate hypotheses, run rigorous backtests, build and maintain live alpha signals and execution models, analyze microstructure and order flow, collaborate with engineers to move research to production, and monitor and iterate on live strategy performance.

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.

Review and evaluate quantitative finance tasks for technical accuracy and real-world realism. Identify missing assumptions, unrealistic constraints, edge cases, and data or backtesting issues. Provide concise, structured written feedback and actionable recommendations. Collaborate with other experts to calibrate evaluations and maintain consistent quality standards throughout a high-engagement, remote contractor sprint.

Own the full accounting function for a quantitative trading firm: run books in Xero, perform monthly P&L reconciliations, manage payroll (US/Canada) in ADP, approve supplier invoices, handle bank and cash operations, maintain internal controls and audit readiness, and resolve discrepancies with external parties.

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

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

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