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Data Scientist (Private Wealth Management)

Responsibilities Formulate and implement the Asset and Wealth Management analytics strategy to drive organizational goals. Leverage big data, machine learning, artificial intelligence, and advanced analytics to uncover profound insights in areas like fraud prevention (including early risk alerts and anti-money laundering), alongside marketing and revenue optimization efforts. Collaborate with business units and stakeholders to identify requirements, then recommend innovative, data-centric approa

Morgan McKinley - Hong Kong - Full time

Salary: Competitive

Responsibilities
  • Formulate and implement the Asset and Wealth Management analytics strategy to drive organizational goals.
  • Leverage big data, machine learning, artificial intelligence, and advanced analytics to uncover profound insights in areas like fraud prevention (including early risk alerts and anti-money laundering), alongside marketing and revenue optimization efforts.
  • Collaborate with business units and stakeholders to identify requirements, then recommend innovative, data-centric approaches to address them.
  • Design and deploy rapid prototypes to demonstrate potential benefits, followed by scaling them into robust production systems for informed decision-making.
Skills Reqd
  • Bachelor's or Master's degree in Computer Science, Finance, Business Analytics, or a closely related field.
  • Brings 3-5 years of hands-on experience developing data analytics and modeling solutions, ideally within Private Wealth Management or a similar high-net-worth client environment.
  • Demonstrates an entrepreneurial, self-driven mindset with strong curiosity for data exploration, capable of independently translating complex data into actionable business insights and strategic recommendations.
  • Solid understanding of the private wealth management sector, combined with deep technical expertise in natural language processing (including Transformer architectures), recurrent networks (LSTM), clustering techniques (K-Means), tree-based ensemble methods (Random Forest, Isolation Forest, Gradient Boosting), as well as strong proficiency in SQL, machine learning workflows, and Python programming.


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