The ideal candidate brings quantitative experience in the Equity business (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 not only to help defining future business practices but also to bring cultural change towards a modern data-driven approach to business.
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 one of C++, Python, R, Java, Scala.
Confident in technology in particular around data management. Knowledge in KDB and Big Data solutions such as Hadoop/Spark, Hive etc advantageous
Previous practical experience in solving machine learning problems using open-source packages (such as sklearn). Experience in TensorFlow or other RL 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
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Please note that J.P. Morgan will not accept unsolicited approaches or speculative CVs, nor will J.P. Morgan be responsible for any related fees, from Third Party Firms who are not preferred suppliers.
The firm invites all interested and qualified candidates to apply for employment opportunities.
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