Assistant Vice President, Data Science & Analysis

Morgan Stanley•Jersey City, NJ

1d•Hybrid

About The Position

Morgan Stanley Smith Barney, LLC is seeking an Assistant Vice President, Data Science & Analysis in Jersey City, New Jersey. The role develops data science solutions that can be tested and deployed to enable data-driven recommendations and outcomes, and implements and scales high-availability models and algorithms for business and corporate functions. It leverages machine learning to deliver use cases related to client acquisition, including marketing, targeting, digital, and product strategy. The position also builds modeling solutions for business problems and leads the development of data science and modeling capabilities as part of the cloud transformation journey. The role investigates and creates experimental prototypes for specific domains and verticals, analyzes complex datasets to identify patterns and trends, enhances existing models, and conducts large-scale experiments to test hypotheses and drive business growth. It collaborates with 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 is permitted up to one day per week.

Requirements

Master’s degree in Data Science, Quantitative Economics, Mathematics, or a related field.
Two years of experience in the position offered, or two years as a Data Science & Analysis Associate, Machine Learning Researcher, or in a related role.
Two years of experience developing end-to-end 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.
Two years of experience developing and benchmarking classification and regression algorithms, including deep learning approaches, using rigorous experimentation to improve performance and robustness.
Two years of experience designing and deploying agentic AI solutions and applying generative AI techniques to enhance machine learning and deep learning model performance, robustness, and development efficiency.
Two years of experience using Python for predictive modeling, data processing, feature engineering, statistical analysis, and workflow automation.
Two years of experience writing SQL queries for data extraction, transformation, aggregation, and analysis on enterprise-scale relational databases.
Two years of experience applying statistical analysis, hypothesis testing, and A/B testing methodologies to evaluate model and business performance.
Two years of experience applying feature engineering, unsupervised learning methods, text processing, and pattern discovery techniques to structured and unstructured datasets.
Two years of experience developing reports and visual analytics to communicate model performance, experimental results, and business insights to technical and non-technical stakeholders.
Two years of experience 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.
Two years of experience validating machine learning models in collaboration with a validation team to ensure accuracy and reliability.

Responsibilities

Develop data science solutions that can be tested and deployed to enable data-driven recommendations and outcomes.
Implement and scale high-availability models and algorithms for business and corporate functions.
Use machine learning modeling and algorithms for client acquisition use cases, including marketing, targeting, digital, and product strategy.
Build modeling solutions to solve business problems and lead development of data science and modeling capabilities in the cloud transformation journey.
Investigate and create experimental prototypes for specific domains and verticals.
Analyze large, complex datasets to identify underlying patterns, correlations, and trends quantitatively.
Support and enhance existing models to improve performance.
Set up and conduct large-scale experiments to test hypotheses and drive business growth.
Collaborate with 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.
Develop machine learning solutions and pipelines spanning data sourcing, feature engineering, evaluation, deployment, and monitoring.
Develop and benchmark classification and regression algorithms, including deep learning approaches, using rigorous experimentation.
Design and deploy agentic AI solutions and apply generative AI techniques to machine learning and deep learning.
Use statistical analysis, hypothesis testing, and A/B testing to evaluate model and business performance.
Develop reports and visual analytics to communicate model performance, experimental results, and business insights.
Partner with stakeholders to gather requirements, communicate findings and model limitations, document decisions, and support governance processes.
Validate machine learning models with the validation team to ensure model accuracy and reliability.

Benefits

Medical insurance
Prescription drug coverage
Dental insurance
Vision insurance
Health Savings Account
Dependent Day Care Savings Account
Life insurance
Disability and other insurance plans
Paid time off, including sick leave consistent with state and local law
Parental leave
20 vacation days annually
10 paid holidays
401(k)
Short- and long-term disability
Potential annual discretionary incentive compensation award
Potential participation in the business unit’s incentive compensation plan, which may include a discretionary bonus
Other employee perks