Machine Learning, Assistant Vice President

Morgan Stanley•New York, NY

1d•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 or a PhD degree (preferred) in Computer Science, Engineering, Mathematics, Physics, or an equivalent quantitative field.
At least 3 years of professional experience in Machine Learning.
Demonstrated breadth and depth in knowledge and applications of machine learning algorithms in classification, regression, recommender systems, clustering, deep learning
Proficiency in autonomously conducting applied ML research with commercial applications.
Proficiency in at least one of the modern programming languages (Python, C++, or a related language).
Experience with code versioning systems such as Github, Bitbucket, and experiment tracking systems like MLFLow.
Proficiency with computer science fundamentals in object-oriented design, data structures, and algorithmic design.
Experience communicating with business stakeholders.
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

Design and develop end-2-end machine learning solutions to address business opportunities in Wealth Management, delivering tangible business outcomes.
Strive to develop and experiment with State-of-the-Art algorithms.
Validate the machine learning models in collaboration with the validation team to ensure the accuracy and reliability of ML models.
Deploy the machine learning models in production environments, in collaboration with the MLOps team, and monitor their performance.
Conduct A/B tests to demonstrate efficacy of ML solutions.
Participate in code reviews from both sides of the process.
Build, grow, and establish partnerships with business stakeholders, marketing as well as with our Risk, Legal, and Compliance divisions.
Create presentations to effectively showcase modelling results to stakeholders and the team.