JPMorgan Quantitative Research Markets Capital - Vice President 

India, Maharashtra, Mumbai 

203409872

As a Quantitative Research professional, you will be working on in building financial engineering, data analytics, statistical modeling and portfolio management.

• Implementation of the next generation of risk analytics platform and assess model performance, perform back testing analysis and P&L attribution;
• Improve performance and scalability of analytics algorithms and Develop and enhance mathematical models for VaR/Stress/FRTB;
• Assess the appropriateness of quantitative models and their limitations, identifying and monitoring the associated model risk;
• Design efficient numerical algorithms and implementing high performance computing solutions;
• Design and develop software frameworks for analytics and their delivery to systems and applications.

• Advanced degree (PhD, MSc, B.Tech or equivalent) in Engineering, Mathematics, Physics, Computer Science, with 7+ years of relevant experience in Python and/or C++ ;
• You demonstrate proficiency in data structures, standard algorithms and object oriented design;
• You understand the different types of risk and you can discuss in detailed ways of managing these risks;
• You have basic understanding of product knowledge across a range of asset classes – Credit, Rates, Equities, Commodities, FX & SPG;
• You’re interested in applying agile development practices;
• You demonstrate quantitative and problem-solving skills as well as research skills;

Preferred qualifications, capabilities, and skills

• Experience applying statistical and/or machine learning techniques in the financial industry and knowledge of options pricing theory, trading algorithms or financial regulations; Experience using multi-threading, GPU, MPI, grid, or other HPC technologies is a plus;
• Excellent knowledge on data analysis tools in python like Pandas, Numpy, Scipy etc
• Knowledge of advanced mathematics such as stochastic calculus;
• Knowledge of front-end technologies like HTML, React and integration with large data sets.