Quantitative Research - Equity Finance - VP
Mumbai, India | J.P. Morgan
Functions:Financial Services Professional
Job Description:71 people have viewed this job
Risk & Pricing: Use probabilities, stochastic processes & other math to model financial instruments & risks.
Given the nature of the business typical risks and payoffs modelled present non-linearities in the rates, borrow and dividend space – but are typically of delta1 nature (linear in the spot).
Models span across single name and portfolio level.
Data Analytics: Analyze data from markets, transactions and business processes, to provide insights into prices and risks, and aid decision making and optimizations.
This is accomplished through a combination of Statistics and Machine Learning techniques.
Trading and Business Optimizations: Determining optimal strategies for interacting with the markets to finance or hedge the book This entails modelling synergiesbetween differentareas,liquidity, balance sheet,aswellaslinearand non-linearconstraints. Thisisdelivered though platforms or algos that fully automate or augment business functions.
The primary responsibilities for this role will include:
Lead forward developments on trading optimization platforms employed by the Equity Finance business to manage SBL, Synthetic and Cash trading activities.
Put in place large and scalable architectures, linearize state-space to deal with massive data sets, vectorised coding and distributed computing.
Liaise with multiple stake-holders to formulate a multi-dimensional objective function across metrics: PnL, Liquidity, RWA, ROA, etc.
Model trading book dynamics, transaction costs potentially employing Statistical and Machine Learning techniques.
Document and test new/existing models with traders and with other various control groups, such as the Model Review Group
Implementation of models in C++ and Python proprietary libraries.
Excellent Math background.
Ability to work with big-data and experience in formulation of optimizations.
Strong experience in linearization of state-space.
Strong analytical and problem solving abilities.
Strong communication/presentation skills.
Strong programming skills (C++, Python).
Experience with parallel computing, vectorization and memory management is positively regarded.
Solid knowledge with CPLEX, GUROBI, MOSEK or other main stream optimization packages is desirable.
Knowledge of Financial Engineering, Secured Financing and Prime is a plus.
Previous experience with formulation of Statistical models / hands-on implementation of Machine Learning neural networks is a plus.
Advanced degree in a technical field from a top-tier school/program: engineering, sciences, computer science, applied math.