Job description

Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management 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. As a market leader, the talent and passion of our people is critical to our success. Together, we share a common set of values rooted in integrity, excellence and strong team ethic. Morgan Stanley can provide a superior foundation for building a professional career - a place for people to learn, achieve and grow. A philosophy that balances personal lifestyles, perspectives and needs is an important part of our culture. 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. 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. Qualifications 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. Preferred 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 WHAT YOU CAN EXPECT FROM MORGAN STANLEY: At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what s best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work. To learn more about our offices across the globe, please copy and paste morganstanley.com into your browser. Expected base pay rates for the role will be between $85,000 and $140,000 per year at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long-term incentive packages, and other Morgan Stanley sponsored benefit programs. Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents. Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences. For more information, please visit : morganstanley.com .

Requirements

Must-haves

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.
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.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.

Nice-to-haves

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.
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

Application prep

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Keywords to mirror

machine learningaimlpythonazureaws

Platform: workday
Effort: long

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