Equity Derivatives Machine Learning – Associate/ Vice President

  • Competitive
  • Hong Kong
  • CDI, Plein-temps
  • JPMorgan.
  • 12 déc. 17 2017-12-12

Exciting opportunity in Quantitative Strategy utilizing Artificial Intelligence and Machine Learning

About J.P. Morgan Chase & Co.
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $2.6 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small business, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world's most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com.
About J.P. Morgan’s Corporate & Investment Bank
J.P. Morgan’s Corporate & Investment Bank is a global leader across banking, markets and i world’s most important corporations, governments and institutions entrust us with their business in more than 100 countries. With nearly $20 trillion of assets under custody, the Corporate & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. 
About The Team
The Quant and Derivative Strategy research team’s mandate is to research and report on new data-driven investment strategies for equities clients. We want to develop research reports and products using new data sets and advancements in machine learning.
The candidate’s key responsibilities would include:

  • Perform large-scale analysis on proprietary datasets

  • Identify new insights that drive feature modelling

  • Build prototype models (Python) with data pipelines to test trading ideas

  • Leverage data visualization to communicate data insights and results  

  • Write research reports on the findings and results of these prototypes

  • Present the research reports to clients

  • Ongoing desk support and request work

Candidate description:
The ideal candidate brings quantitative experience in the Equities (products, models, market standards and business practices), combined with a background in machine learning techniques / statistics, and a curiosity to expand in this field.
Communication skills and drive are critical for the role as we expect the candidate to work with the team to develop new trading ideas and communicate these to clients both in writing and in person.

  • A master’s or Ph.D. degree program in computer science, statistics, operations research or other quantitative fields  

  • Strong technical skills in data manipulation, extraction and analysis   • Fundamental understanding of statistics, optimization and machine learning methodologies  

  • Mastery of software design principles and development skills using Python and SQL.

  • Confident in technology in particular around data management. Knowledge in KDB and Big Data solutions such as Hadoop/Spark, Hive etc advantageous  

  • Experience in working with equity specific alternative data sets and traditional fundamental data

  • Experience with data vendors like Bloomberg and Factset will be useful

  • Previous practical experience in solving machine learning problems using open-source packages (sklearn…)

  • Experience in TensorFlow or other related packages is advantageous

  • Participation in KDD/Kaggle competition or contribution to GitHub highly desirable  

  • Strong communication skills (both verbal and written) and the ability to present findings to a non-technical audience