VP - Data Science

Key Skills

Java Programming, Python Programming, SQL, Azure, R Programming

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, to achieve and grow. A philosophy that balances personal lifestyles, perspectives and needs is an important part of our culture.

The Analytics & Data Organization is part of Morgan Stanley’s Wealth Management Division. The organization’s responsibility is to support Wealth Management’s strategic objectives through the use and exploitation of its data and analytical assets. Key focus areas will be in support of organic growth through the digitization and delivery of investment advice; partnering with each business area by providing them the tools, expertise, and support for them to understand and analyze their own data; and to make data and analytics a differentiated capability for the firm.

Morgan Stanley is currently searching for AVP, Data Science. This role will directly report to the Executive Director, Data Science in Enterprise Data & Analytics (EDA) organization. The person will work on data science use-cases for the enterprise and must have deep expertise in supervised and unsupervised machine learning, modeling and algorithms with a strong focus on delivering use-cases and solutions at speed and scale to solve business problems. The person must have ability to learn at rapid pace, develop strong business acumen with the ability to collaborate with multiple stakeholders across the enterprise - marketing, digital, product, sales & service, EDA, finance, and technology to drive business results

Job Responsibilities:

• Leverage AI/ML modeling and algorithms to deliver on use cases for the Retail Growth plan across marketing, targeting, digital and products strategy
• Build modeling solutions at speed and scale to solve business problems
• Lead the development of data science and modeling capabilities in the cloud transformation journey
• Develop data science solutions that can be tested and deployed to enable data-driven recommendations and outcomes
• Implement and scale-up high-availability models and algorithms for various business and corporate functions
• Investigate and create experimental prototypes that work on specific domains and verticals
• Analyze large, complex data sets to reveal underlying patterns, correlations, and trends quantitatively
• Support and enhance existing models to ensure better performance
• Set up and conduct large-scale experiments to test hypotheses and drive business growth

QUALIFICATIONS

• Degree (Graduate / Ph.D. preferred) in an analytical or technical field (e.g. applied mathematics, physics, computer science, operations research)
• Advanced knowledge of statistical and machine learning methods, particularly in the areas of modeling
• Hands on experience on building models at speed and scale
• Strategic thinker and influencer with demonstrated leadership acumen and problem-solving skills
• Ability to work in a collaborative, transparent style with cross-functional stakeholders across the organization to lead and deliver results
• Strong skills in oral and written communication including experience in effectively communicating with executives, employees, colleagues, and clients
• Ability to lead a high-functioning team and develop and train people
• 8+ years of experience programming in SQL, and Python and R
• 8+ years of experience working with relational databases and distributed computing platforms and their query interfaces
• Experience conceiving, implementing and continually improving machine learning projects
• Strong familiarity with higher level trends in artificial intelligence and open-source platforms
• Experience working with AWS, Azure, or similar cloud platform
• Familiarity with visualization techniques and software