Machine Learning, Vice President
Morgan Stanley · New York, United States · 17d ago
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.
Qualifications
- 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
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 https://www.morganstanley.com/about-us/global-offices into your browser.
Expected base pay rates for the role will be between $115,000 and $190,000 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: https://www.morganstanley.com/people-opportunities/eeo.