Top Quant Finance Jobs in NYC, NY

Lead the design, development, implementation, back testing, and enhancement of regulatory VaR and market risk models for structured products, including RMBS and other securitized assets. Develop pricing, OAS, prepayment, default, spread, volatility, scenario, and risk sensitivity analytics. Translate regulatory and business requirements into production controls, documentation, validation evidence, and monitoring frameworks. Advise trading, risk, technology, audit, senior leadership, and regulators on model assumptions, limitations, performance, and governance.

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.

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.

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

Develop and improve quantitative equity investment strategies across the full research lifecycle, including idea generation, complex data sourcing, signal construction, statistical and machine learning analysis, portfolio construction, risk modeling, trading cost analysis, and execution. The role requires presenting research findings, collaborating with investment research teams, and applying advanced quantitative methods to real investment problems.

Supports portfolio managers and the CIO in evaluating, selecting, monitoring, and negotiating investments with external hedge funds, including quantitative and CTA strategies. Conducts manager research, develops investment assessment and return forecasting frameworks, and makes evidence-based hiring or divestment recommendations. Also supports internal macro trading research by optimizing quantitative strategies and managing automated analytics. The role requires strong quantitative investment expertise, communication skills, decisiveness, and interest in owning investment decisions.

Develops quantitative models, analytics, and automated trading capabilities for U.S. Treasury electronic trading. Responsibilities include RFQ pricing, spread optimization, auto-hedging, inventory management, execution strategy, market data analysis, monitoring tools, production solution support, and model governance. Partners with traders, technology, and sales teams to improve profitability, execution quality, risk-adjusted returns, and client outcomes.

Develop and support quantitative trading tools for equity derivatives desks, including intraday risk, valuation, P&L attribution, portfolio hedging, reporting, and model diagnostics. Collaborate with traders and technology teams to troubleshoot issues, prototype scalable solutions, optimize workflows, review back-test data, and validate pricing models and parameters.

Develop and implement quantitative models, risk analytics, hedging tools, portfolio valuation solutions, and optimization capabilities for capital markets risk platforms. Integrate models into production systems with engineering teams, modernize risk technology architecture, validate methodologies, improve performance, and support analytics across multiple asset classes while collaborating with Risk, Technology, and Front Office stakeholders.

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.

Research and deploy systematic DeFi trading strategies using on-chain signals, protocol mechanics, tokenomics, liquidity flows, MEV, funding rates, and cross-protocol arbitrage. Responsibilities include backtesting with gas and slippage constraints, collaborating on live deployment, monitoring performance, and contributing to on-chain data infrastructure. The role requires strong mathematical or scientific training, Python, deep DeFi expertise, statistical rigor, and independent research capability.

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.

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.

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.

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.

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.

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.