Machine Learning, Vice President

Morgan Stanley•New York, NY

2d•$115,000 - $190,000•Onsite

About The Position

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.

Requirements

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, recommender systems, clustering, deep learning, and experimental design.
Demonstrated hands-on experience building models at speed and scale to solve complex commercial or business problems.
Experience conceiving, implementing, deploying, and continually improving machine learning projects in production or production-like environments.
Minimum of 8 years of experience programming in SQL, Python, and/or R.
Proficiency in autonomously conducting applied ML research with commercial applications and translating business problems into scalable modeling solutions.
Strong familiarity with higher-level trends in artificial intelligence, generative AI, LLMs, and open-source AI/ML platforms.
Experience working with AWS, Azure, Google Cloud, or similar cloud platforms.
Experience with code versioning systems such as GitHub or Bitbucket, and experiment tracking systems such as MLflow or equivalent.
Proficiency with computer science fundamentals, including object-oriented design, data structures, and algorithmic design.
Strategic thinker and influencer with demonstrated leadership acumen, problem-solving skills, and ability to drive outcomes across cross-functional teams.
Strong communication skills with experience presenting technical concepts, modeling results, and business recommendations to senior business stakeholders.
Familiarity with visualization techniques and software to communicate analytical insights effectively.
Proficiency in English

Nice To Haves

Experience with Cloud or Big Data technologies such as Azure, AWS, Google Coud, Hadoop, or an equivalent
Familiarity with Deep Learning frameworks (PyTorch, Tensorflow, PyTorch – Geometric, or equivalent).
Experience with Graphical Neural Networks, Reinforcement Learning, LLMs, Transformer based Models, or Recommender Systems is a plus.
Track record of publishing in peer-reviewed scientific journals

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.