Morgan Stanley
Machine Learning, Assistant Vice President
Morgan Stanley, Jersey City, New Jersey, United States, 07390
Machine Learning, Assistant Vice President
role at
Morgan Stanley
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 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 client channels and product organizations, delivering delightful experiences to over 20M WM clients.
Responsibilities
Design and develop end‑to‑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 accuracy and reliability.
Deploy the machine learning models in production environments, collaborating with the MLOps team, and monitor their performance.
Conduct A/B tests to demonstrate efficacy of ML solutions.
Participate in code reviews throughout the process.
Build, grow, and establish partnerships with business stakeholders, marketing, and with Risk, Legal, and Compliance divisions.
Create presentations to effectively showcase modelling results to stakeholders and the team.
Qualifications
Master’s or 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 modern programming language (Python, C++, or related).
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.
Preferred
Experience with Cloud or Big Data technologies such as Azure, AWS, Google Cloud, Hadoop, or 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.
What You Can Expect From Morgan Stanley We are committed to maintaining first‑class service and a high standard of excellence that has defined Morgan Stanley for over 89 years. Our values guide the decisions we make every day: putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back. You’ll find an opportunity to work alongside the best and the brightest in an environment that supports and empowers you.
Our employees enjoy attractive and comprehensive benefits and perks, including medical, prescription drug, dental, vision, health savings account, disability, paid time off, 401(k), and more.
To learn more about our offices across the globe visit https://www.morganstanley.com/about-us/global-offices.
Compensation Salary range: $85,000 - $140,000 (NY). Eligible for discretionary incentive compensation and incentive plan participation. Full spectrum of benefits available.
Referrals increase your chances of interviewing by 2×.
Equal Opportunity Employer Morgan Stanley is an equal opportunity employer committed to diversifying its workforce (M/F/Disability/Vet). It is the policy of the Firm to provide equal employment opportunity without discrimination or harassment on the basis of race, color, religion, creed, age, sex, sex stereotype, gender, gender identity or expression, transgender, sexual orientation, national origin, citizenship, disability, marital and civil partnership/union status, pregnancy, veteran or military service status, genetic information, or any other characteristic protected by law.
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role at
Morgan Stanley
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 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 client channels and product organizations, delivering delightful experiences to over 20M WM clients.
Responsibilities
Design and develop end‑to‑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 accuracy and reliability.
Deploy the machine learning models in production environments, collaborating with the MLOps team, and monitor their performance.
Conduct A/B tests to demonstrate efficacy of ML solutions.
Participate in code reviews throughout the process.
Build, grow, and establish partnerships with business stakeholders, marketing, and with Risk, Legal, and Compliance divisions.
Create presentations to effectively showcase modelling results to stakeholders and the team.
Qualifications
Master’s or 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 modern programming language (Python, C++, or related).
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.
Preferred
Experience with Cloud or Big Data technologies such as Azure, AWS, Google Cloud, Hadoop, or 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.
What You Can Expect From Morgan Stanley We are committed to maintaining first‑class service and a high standard of excellence that has defined Morgan Stanley for over 89 years. Our values guide the decisions we make every day: putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back. You’ll find an opportunity to work alongside the best and the brightest in an environment that supports and empowers you.
Our employees enjoy attractive and comprehensive benefits and perks, including medical, prescription drug, dental, vision, health savings account, disability, paid time off, 401(k), and more.
To learn more about our offices across the globe visit https://www.morganstanley.com/about-us/global-offices.
Compensation Salary range: $85,000 - $140,000 (NY). Eligible for discretionary incentive compensation and incentive plan participation. Full spectrum of benefits available.
Referrals increase your chances of interviewing by 2×.
Equal Opportunity Employer Morgan Stanley is an equal opportunity employer committed to diversifying its workforce (M/F/Disability/Vet). It is the policy of the Firm to provide equal employment opportunity without discrimination or harassment on the basis of race, color, religion, creed, age, sex, sex stereotype, gender, gender identity or expression, transgender, sexual orientation, national origin, citizenship, disability, marital and civil partnership/union status, pregnancy, veteran or military service status, genetic information, or any other characteristic protected by law.
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