The role involves designing and developing end-to-end machine learning solutions that address key Wealth Management business opportunities and produce measurable outcomes
Job Summary
The role involves designing and developing end-to-end machine learning solutions that address key Wealth Management business opportunities and produce measurable outcomes.
Candidates will partner with model risk stakeholders to validate models, ensuring reliability and meeting strict governance expectations before deployment.
Morgan Stanley offers a supportive environment where employees can move across businesses and leverage diverse backgrounds to make a global impact.
Matching Summary
The role involves designing and developing end-to-end machine learning solutions that address key Wealth Management business opportunities and produce measurable outcomes.
Skills & Requirements
Must-have
End-to-end machine learning solution development
Python programming proficiency
Model risk validation and governance
MLOps deployment and monitoring
A/B testing for ML solutions
Nice-to-have
LLM and Generative AI experience
Cloud platform knowledge (Azure/AWS)
Deep learning framework expertise
Peer-reviewed publication track record
Client-facing communication skills
Key Requirements
Master's or PhD in Computer Science or quantitative field
Minimum 5 years professional Machine Learning experience
10-14 years total professional experience preferred
Experience in financial services industry preferred