Description

Design, deploy, and lead advanced machine learning and AI initiatives for Wealth Management use cases, including portfolio construction, client segmentation, and predictive analytics.

This role is hybrid.

Responsibilities

• Design and deploy ML models for portfolio optimization, risk analytics, and client personalization.
• Partner with Wealth Management, product, and advisory teams to frame and execute high-value analytics use cases.
• Guide feature engineering, model validation, and continuous improvement through experimentation and hypothesis testing.
• Collaborate with Engineering and MLOps teams to operationalize models, ensuring scalability, data quality, and audit readiness.
• Mentor junior data scientists and translate complex analytical outputs into clear business insights for senior stakeholders.

Required Skills

• 8–12+ years of experience in data science, advanced analytics, or quantitative roles.
• Proven Wealth Management or Investment domain experience (portfolios, advisory, or client analytics).
• Strong proficiency in Python or R with a solid statistical and analytical foundation.
• Experience applying ML/statistical models in large-scale enterprise environments.
• Hands-on experience with Data Platforms and modern analytics pipelines.
• Practical experience with MLOps for model operationalization and governance.
• Bachelor's degree in a quantitative field.

Key Skills

Education

Bachelor's degree