Job Title

As a QTR member, you will drive innovation across thevol tradingecosystem by applying advanced data analytics, statistical modeling, and machine learning. Join our global team and leverage your skills to shape the future of financial markets.We offer comprehensive training and growth opportunities to enhance your skills and advance your career. Our diverse team supports a wide range of business functions, providing a unique environment for professional development. We are committed to accommodating diverse needs and fostering an inclusive workplace.Job SummaryAs an Alpha Quant on the Quantitative Trading & Research (QTR) Equity Derivatives team, you will focus onend-to-end alpha research and strategy deploymentacross equity options and volatility markets. You will help drive the alpha research agenda for Systematic Derivatives, using data analytics and software engineering to deliverresearch-to-productionstrategies. Your role will involve feature engineering from diverse data sources, building robustalpha calibration, attribution, and monitoring frameworks, partnering closely with trading, and implementing systematic strategies with strong attention to execution, hedging, and risk.Job ResponsibilitiesWork closely with trading to buildend-to-end design and implementationof daily and intradaysignal research and deployment infrastructure, with special focus onequity derivatives / Systematic derivatives.Contribute from idea generation to production implementation: perform research, design prototypes, implement alpha signals and systematic strategies; support daily usage, monitor performance, and iterate based on live feedback.Research and modelequity options and volatility dynamics(., surface arbitrage, term structure, skew, dispersion, event risk, RV) and translate insights into deployable systematic strategies.Develop and maintain robustbacktesting, attribution, and regime analysisframeworks tailored to derivatives PnL drivers.Build models that integratefundamental, quantitative, and microstructure featuresto support risk internalization and/or risk warehousing, using statistics, machine learning, or heuristics as appropriate.Partner with the business onalpha capture, risk recycling, hedging design, and position/risk management for derivatives strategies (including Greeks and scenarios).Collaborate broadly with QTR teams across regions to build reusable research libraries, tooling, and standardized workflows for experimentation, deployment, and monitoring.(Plus) LeverageAI/ML and modern AI toolingto accelerate research and improve developer productivity, with an understanding ofAI product ionization(model governance, evaluation, monitoring, and safe professional use of AI agents).Required Qualifications, Capabilities, and SkillsYou have a strong quantitative background, as well as practical problem-solving skills.You have direct working knowledge ofsignal research with market data and other financial data, alpha capture, and risk warehousing, preferably in equity derivatives.You like working closely with trading desks, understanding their business, and have a strong mind-set of ownership to have an impact on the way they operate.You demonstrate proficiency in code design and programming skills, with primary focus onPython, KDB, C++ or Javain a commercial environment.You have practical data analytics skills on real data sets gained through hands-on experience, and can handle and analyze complex, large scale, high-dimensionality data from various sources.You quickly grasp business concepts outside immediate area of expertise and adapt to rapidly changing business needs.You think strategically and creatively when faced with problems and opportunities. You always look for new ways of doing things.Your excellent communication skills, both verbal and written, can engage and influence partners and stakeholders.Preferred Qualifications, Capabilities, and SkillsStrong graduate degree (MS or PhD) in a quantitative field (Computer Science, Financial Engineering, Mathematics, Physics, Statistics, Economics, …).Strong expertise in statistics and machine learning in financial industry.Robust testing and verification practice.Direct experience with electronic trading, and knowledge of trading algorithms.3 to 5 years’ experience in finance: market making, electronic trading, trading strategies (high to low frequency: market making, statistical arbitrage,option trading…), orderivatives pricing and risk management.Knowledge ofequity derivatives and volatility productsis a plus.Plus: experience leveragingAIfor research and engineering workflows, and familiarity withproductionizing AI(repeatable pipelines, evaluation/monitoring, model risk awareness) and usingAI agents professionally.