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Vice President – Digital Advice Analytics
Morgan Stanley
New York, NY, United States
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Description
Morgan Stanley Wealth Management provides comprehensive brokerage, investment and financial services to individual investors globally. Recognized as one of the largest retail brokerage networks in the U.S., Morgan Stanley’s financial representatives cover multiple client segments from affluent to ultra- high net worth. The product range offered to client’s spans mutual funds, equities, fixed income products, alternative investments, separately managed accounts, banking & personal lending, mortgages, insurance and annuities.
The Decision Sciences team goal is to help the organization make fact based decision around risk and opportunities, which help drive top and bottom line growth. The incumbent is expected to mine Morgan Stanley’s book of business to identify new or existing customer opportunities.
The Decision Sciences team has an opportunity for a talented individual that will play a key role in the area of digital advice analytics, with the goal of understanding behavior-driven insights within the e-channel space and find creative ways to help management interpret the data further.
The ideal candidate will possess strong leadership, communication, project management skills, and a thorough understanding of applied statistics theory - power user of SAS or related tools (e.g. R) based statistics. Candidate must have significant hands-on experience managing large scale cross-product analyses with ability to influence people across all levels of the organization. In addition, this candidate must have proven skills in exploratory analytics with a specific and demonstrated focus in the ability to think outside the box.
General Responsibilities
• Development, management and reporting of all research for the purposes of
improving the Firm’s understanding of tactical and strategic initiatives
• Develop thorough understanding of the portfolio performance through client
segmentation and key performance metrics tracking
• Seek opportunities to deploy analytics to drive decisions in everyday field
management and client management; help establish advanced platform to
deliver insights to the field in real time
• Evaluate effectiveness of advisor practices through client behavior changes
and business results. Find scalable solutions to improve advisor performance
• Use predictive analytics to drive client contact/offer targeting for maximized
return
• Stay tuned with industry trending, opportunities and challenges. Provide
business opportunity/risk sizing for the senior management
• Interact with a wide variety of business units to derive and implement analytics
solutions
Digital Advice Responsibilities
• Prospect Acquisition
◦ Optimize sales channel efficiency through the development of models to score
and rank new leads
◦ Help design and implement experiments to optimize user journeys and
downstream conversion rates
• Client Insights & Reporting
◦ Model customer lifetime value and develop forecasting models to predict
expected value of new-to-firm clients based on client profile data, behavioral
data, 3rd party data, etc.
◦ Analyze client account data – such as cash flow transactions, purchase
behavior, and goal progress – to develop predictive models that help clients
understand and weight the tradeoffs of different saving, investing and
spending decisions
◦ Develop lookalike models that help clients understand how their personal
financial management decisions relate to those of other individuals
◦ Analyze attrition data to understand drivers of attrition, and suggest
opportunities to improve retention
◦ Use client data to identify product cross-sell opportunities
◦ Use first- and third-party data to develop estimates of held-away assets
◦ Develop a segmentation framework for digital advice clients to help inform
advertising and product investment decision-making
• Managerial Reporting
◦ Develop managerial reporting dashboards on conversion funnel metrics to explain advertising efficacy and marketing ROI, source of leads, asset-flows and attrition, etc.
◦ Analyze clickstream data to identify problem areas and/or improve product usage
Qualifications
• Advance degree in Statistics, Economics or related
• Power user of SQL, SAS, and other statistical software packages. Enterprise
Miner experience a plus
• Demonstrated ability to collect and organize data, conduct analysis and report
on and apply results to “actionable insights/recommendations”
• Solid predictive modeling experience is required with applying Decision Trees,
Regression analysis, other data mining techniques, Understanding of time
series and experimental design analysis necessary. Proficiency in Python
desirable.
• Need to be able to succinctly communicate ideas, recommendations, articulate
analytics results to business people and influence decision making
• Self-starter with strong and creative problem-solving skills
• 10+ years of related work experience
• Minimum of 2 – 4 years of e-commerce and e-channel analytics background
and experience
• Related financial services industry experience for at least 3+ years is Required