Asset Management – Data Scientist Lead – Vice President

Found in: Talent HK C2 - 2 weeks ago


Hong Kong, Hong Kong SAR China JPMorgan Chase & Co. Full time

The . Morgan Asset Management ("JPMAM") Data Science Team is part of . Morgan Asset & Wealth Management. The JPMAM Data Science team focuses primarily on developing novel Artificial intelligence/Machine Learning methods to drive innovative solutions for amplifying data-driven investment decision-making, improved client engagement, and operational effectiveness.

As a Data Scientist Lead - Vice President in the . Morgan Asset Management Data Science Team, you will work closely with investment and data science professionals across Asset Management to proactively source, due diligence and draw insights from in-house investment and alternative data. This is an exciting opportunity to join a small, dynamic team with the resources and impact of one of the world’s largest companies. We are looking for motivated individuals to push the boundaries of what is possible in Asset Management.

Job Responsibilities

Work with portfolio managers to understand sources of alpha and opportunities to improve decision-making process Build tools and systems to understand decision data, context and events around it to enhance JP Morgan Asset Management’s decision attribution capabilities and systematically identify opportunities  Work with partners in technology and user experience to build out tools providing real-time insights to portfolio managers and their teams Take part in projects along the full data science spectrum. From data acquisition and wrangling, to model selection to presentation and data visualization.  Work as part of a globally distributed data science team. 

Required qualifications, capabilities and skills 

Masters Degree or PhD, in computer science, statistics, or other quantitative field Strong analytical/modelling skills and business orientation with proven ability to use data and analytics to drive business results; strong technical background Demonstrate solid experience working within a data science team, including Timeseries analysis and modelling; Training and fine-tuning of the Machine Learning model for investment models; Strong knowledge of Python for data scientists (., pandas), traditional Machine Learning and deep learning libraries (., scikit learn, xgboost, TensorFlow, Torch, ; Data manipulation languages (., SQL); Data visualization / presentation skills (., Tableau)  Demonstrate experience working with engineering, developers and other technology teams - Writing production quality code, unit testing and familiarity with version control; Familiar with cloud-based technologies. Strong communications skills (both verbal and written) and the ability to present findings to a non-technical audience Passion for learning and adopting a wide range of techniques in an agile environment

Preferred qualifications, capabilities and skills 

Prior experience working in alpha capture, performance attribution or trading / decision analytics role Front office experience in finance (preferably buy side) Familiarity in incorporating data into investment research Experience in using Snowflake and Snowpark  CFA or equivalent
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