Quantitative Trading & Research - eTrading - Vice President at JPMC Candidate Experience page

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

05 Jul, 26

Salary

0.0

Posted On

06 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Algorithmic trading, Quantitative research, Data analytics, Mathematical modeling, Java, C++, Kdb+/q, Python, Cash equity markets, Market microstructure, Statistics, Econometrics, Stochastic control, Optimization techniques, Machine learning, Deep neural networks

Industry

Financial Services

Description

The Quantitative Trading & Research (QTR) eTrading Team design and implement trading algorithms to integrate client solutions across various functions. This team contributes to the design and implementation of the algorithmic trading products where they integrate quantitative research and data analytics for client solutions across various functions in electronic trading. Our team partners with the electronic trading desk and technology teams to develop sophisticated mathematical models, cutting-edge methodologies and infrastructure to improve the performance of algorithmic trading strategies and promote advanced electronic solutions to our clients worldwide. The Quantitative Researcher function within the QTR eTrading team works closely with other quant researchers, the algo development team and the product team to deliver global solutions for our clients. As a Vice President in the QR eTrading team, you will lead the design and implementation of the algorithmic trading platform, integrating quantitative research, data analytics, and client solutions across various functions within eTrading. You will apply optimization techniques to enhance trade scheduling for both single stocks and portfolio trading in the algo engine. Collaborating with researchers in the QR team and working closely with the electronic trading product and algo development technology teams, you will have the opportunity to guide and shape the direction of the platform. Job Responsibilities * Lead the QR eTrading team locally to drive algo execution product research and development * Partner closely with the product team and trading desks to design and build client centric solutions * Collaborate with quant researchers and trading desks to refine models and strategies that enhance our algorithm performance * Build robust algorithms on cutting-edge execution platform by collaborating closely with our technology partners to integrate quantitative models and deliver optimal solutions within the algo trading engine * Support diagnosis of trading decisions by explaining model and algorithm behavior, conducting scenario analyses, and developing quantitative tools and data analytics. * Validate production implementations for fidelity with the original research specifications. Required qualifications, capabilities, and skills * Masters in STEM subjects such as computer science, engineering, mathematics/statistics, physics, operations research * 5 years of experience in position(s) on algorithmic execution strategies or trading platforms * Knowledge of cash equity markets, microstructure and market impact. * Relevant experience in quantitative research, electronic trading, or related fields. * Advanced knowledge of mathematics and statistics (probability theory, time series, econometrics, causal inference). * Exceptional analytical, quantitative and problem-solving skills to break down complex high dimensional decision variable problems into smaller tractable subproblems. * Strong written and verbal communication skills, with proven ability to communicate complex research ideas clearly and precisely, both in writing and verbally. * Experience with kdb+/q. * Experience coding in Java or C++ Preferred qualifications, capabilities, and skills * Experience with Python, AWS and/or other database/data processing technologies * Experience with alpha researches and their application in algo executions * Experience with stochastic control, stochastic/numerical optimization techniques relevant to single stock or portfolio trading strategies * Experience with writing production grade implementations for trading systems in Java/C++ * Familiarity with machine learning and deep neural networks. J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs [https://careers.jpmorgan.com/us/en/how-we-hire/faqs] for more information about requesting an accommodation. J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.

Responsibilities

Lead the quantitative research eTrading team to design and implement algorithmic trading platforms and client-centric solutions. Collaborate with trading desks and technology teams to refine models and enhance the performance of execution algorithms.