Assistant Vice President, Data Science & Analysis

Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment, 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. In Wealth Management, we help people, businesses and institutions build, preserve, and manage wealth so they can pursue their financial goals.

Morgan Stanley Smith Barney, LLC is seeking an Assistant Vice President, Data Science & Analysis in Jersey City, New Jersey to develop data science solutions that can be tested and deployed to enable data driven recommendations and outcomes and implement and scale-up high availability models and algorithms for various business and corporate functions. Leverage machine learning modelling and algorithms to deliver on use cases related to client acquisition, including marketing, targeting, digital and products strategy.

Build modeling solutions to solve business problems and lead the development of data science and modeling capabilities in the cloud transformation journey. Investigate and create experimental prototypes that work on specific domains and verticals. Analyze large, complex datasets to reveal underlying patterns, correlations, and trends quantitatively. Support and enhance existing models to ensure better performance. Set up and conduct large-scale experiments to test hypotheses and drive business growth.

Collaborate with cross-functional stakeholders (Legal, Compliance, Risk, Technology, and Business Partners) to translate business needs into analytics solutions, align on model governance and controls, and ensure timely, compliant delivery. Telecommuting permitted up to 1 day per week.

• Requires a Master’s in Data Science, Quantitative Economics, Mathematics, or a related field.
• Requires two (2) years of experience in the position offered or two (2) years as a Data Science & Analysis Associate, Machine Learning Researcher, or a related role.
• Requires two (2) years of experience with the following skills:
  End-to-end development of machine learning solutions by translating business opportunities into production-grade models and pipelines including data sourcing, feature engineering, evaluation, deployment, and monitoring with success metrics tied to measurable business outcomes;
  Developing and benchmarking algorithms including deep learning approaches for classification and regression, using rigorous experimentation to improve performance and robustness;
  Designing and deploying agentic AI solutions and applying generative AI techniques to enhance machine learning and deep learning model performance, robustness, and development efficiency;
  Python for predictive modeling, data processing, feature engineering, statistical analysis, and workflow automation;
  Writing SQL queries for data extraction, transformation, aggregation, and analysis on enterprise-scale relational databases;
  Applying statistical analysis, hypothesis testing, and A/B testing methodologies to evaluate model and business performance;
  Applying feature engineering, unsupervised learning methods, text processing, and pattern discovery techniques on structured and unstructured datasets;
  Developing reports and visual analytics to communicate model performance, experimental results, and business insights to technical and non-technical stakeholders; and Partnering with stakeholders to gather requirements, communicate analytical findings and model limitations, document decisions, and support governance processes for production analytics and machine learning solutions; and Validating the machine learning models…