Machine Learning, Vice President

Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management and wealth management services. The Firm's employees serve clients worldwide including corporations, governments and individuals from more than 1,200 offices in 43 countries.

As a market leader, the talent and passion of our people is critical to our success. Together, we share a common set of values rooted in integrity, excellence and strong team ethic. Morgan Stanley can provide a superior foundation for building a professional career - a place for people to learn, achieve and grow. A philosophy that balances personal lifestyles, perspectives and needs is an important part of our culture.

The Machine Learning team in the Wealth Management (WM) Strategy & Analytics division at Morgan Stanley works on a breadth of applied AI research areas including but not limited to recommender systems, client personalization, graphical neural networks (GNNs), and natural language understanding/LLMs. We provide machine learning (ML) solutions to our internal stakeholders across all our clients channels (Advisor-led, Workplace, and Self-directed) and Product organizations (Investment Solutions, Bank) as well as functions (Marketing, Risk).

Our ML scientists ideate, innovate, design, prototype, and ship ML solutions delivering delightful new experiences to 20M+ WM clients.

Responsibilities

• Lead the design, development, and delivery of end-to-end machine learning solutions to address strategic business opportunities in Wealth Management, delivering measurable business outcomes.
• Leverage AI/ML modeling and algorithms to deliver use cases supporting the growth plan across Client advisor and product strategy.
• Build modeling solutions at speed and scale to solve complex business problems across large client and advisor populations.
• Investigate, design, and create experimental prototypes focused on specific business domains and verticals.
• Analyze large, complex data sets to quantitatively reveal underlying patterns, correlations, trends, and growth opportunities.
• Strive to develop and experiment with state-of-the-art algorithms, including advanced machine learning, deep learning, recommender systems, and emerging AI approaches.
• Support and enhance existing models to ensure improved performance, stability, scalability, and business impact.
• Set up and conduct large-scale experiments, including A/B tests, to test hypotheses and drive business growth.
• Validate machine learning models in collaboration with validation teams to ensure accuracy, reliability, explainability, and compliance with model governance standards.
• Deploy machine learning models in production environments in collaboration with MLOps and technology teams, and monitor performance over time.
• Participate in and lead code reviews, modeling reviews, and technical design discussions to raise engineering and modeling standards across the team.
• Build, grow, and strengthen partnerships with business stakeholders, Marketing, Digital, Product, Risk, Legal, Compliance, Technology, and other cross-functional partners.
• Create executive-ready presentations and analytical narratives to effectively communicate modeling results, business implications, and strategic recommendations to senior stakeholders.
• Mentor junior data scientists and contribute to the development of team best practices, reusable modeling assets, and scalable AI/ML frameworks.

Qualifications

• Master’s degree or Ph.D. preferred in an analytical or technical field such as Computer Science, Engineering, Applied Mathematics, Physics, Statistics, Operations Research, or an equivalent quantitative discipline.
• Minimum of 8 years of professional experience in data science, machine learning, AI, advanced analytics, or a related quantitative field.
• Advanced knowledge of statistical and machine learning methods, particularly in modeling, classification, regression, recommende