AWM - Data Scientist - VP

Employer
J.P.Morgan
Location
Hong Kong, Hong Kong
Salary
Competitive
Posted
Jul 13, 2021
Closes
Jul 27, 2021
Ref
11473955
Job Function
Banking
Industry Sector
Finance - General
Employment Type
Full Time
Education
Bachelors
What You Can Expect

To work alongside our top tier professionals, including data scientists, investment professionals, and marketing analytics teams, driving product innovation and identifying opportunities to incorporate machine learning into J.P. Morgan Asset Management.

The successful applicant will form a part of our newly created China research pod. The team will specialize in applying data science techniques to help build out our understanding of the Chinese investment landscape. Typical projects could include:
  • Helping to discover, develop and build out new data sets
  • Use of ML methods to build now-cast or forecast models for predicting company fundamentals
  • Development of proprietary ESG signals on Chinese data
  • Implementation of NLP on Chinese language documents
  • Developing tools to assist our sales teams
Our technology stack is a mixture of cloud (AWS) and on premise with research undertaken using Python.

About You

We're looking for problem-solvers with a passion for developing innovative solutions!

Qualifications:
  • Degree in a numerate discipline preferred.
  • Fluent in Mandarin preferred as this role will engage Mandarin speaking stakeholders a lot
  • Solid understanding of data science fundamentals, core libraries and tools
  • Demonstrable passion and expertise in data science (e.g. through Kaggle or open source participation)
  • Experience working in a collaborative, technical environment (data science, quant or technology)
  • Ability to explain complex subject matter to a non-technical audience; ability to tell a story with data
Highly advantageous:
  • Front office experience in finance (buyside or sellside)
  • Knowledge of Chinese alternative data landscape
  • CFA
  • Experience with Elastic, Pytorch/Tensorflow and or docker

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