Quantitative Research- Market Capital - Vice President

Are you seeking a thrilling opportunity to become a Quant Modelling Vice President in the QR Markets Capital (QRMC) team, where you will embark on implementing the next generation of a risk analytics platform The QRMC team is dedicated to constructing models and infrastructure for managing Market Risk, including Value at Risk (VAR), Stress, and Fundamental Review of the Trading Book (FRTB). As a key member of the QRMC team in India, you will play a vital role in supporting the global activities of the QRMC group. Collaboration with the Front Office and Market Risk functions to create tools and utilities for model development and risk management will also be a significant aspect of this role. Your responsibilities will include working on the implementation of a cutting-edge risk analytics platform, evaluating model performance, conducting back testing analysis and P&L attribution, enhancing the performance and scalability of analytics algorithms, developing mathematical models for VaR/Stress/FRTB, assessing the suitability and limitations of quantitative models, designing efficient numerical algorithms, and creating software frameworks for analytics delivery to systems and applications. To qualify for this role, you should possess an advanced degree (PhD, MSc, B.Tech or equivalent) in Engineering, Mathematics, Physics, Computer Science, or related fields, along with at least 3 years of experience in Python and/or C++. Proficiency in data structures, standard algorithms, and object-oriented design is essential. A basic understanding of product knowledge across various asset classes such as Credit, Rates, Equities, Commodities, FX & SPG is required. Additionally, familiarity with agile development practices, strong quantitative and problem-solving skills, research skills, basic mathematics (statistics, probability theory), good interpersonal and communication skills, and the ability to work effectively in a team are necessary for this role. Attention to detail and adaptability are also key qualities. Preferred qualifications include experience in applying statistical and/or machine learning techniques in the financial industry, knowledge of options pricing theory, trading algorithms, or financial regulations, familiarity with multi-threading, GPU, MPI, grid, or other HPC technologies, excellent knowledge of data analysis tools in Python like Pandas, Numpy, Scipy, advanced mathematics such as stochastic calculus, and understanding of front-end technologies like HTML, React, and integration with large data sets.,