Data Science Vice President - Wealth Management

Data can change the way clients experience wealth management—when it is translated into decisions people can act on. In this role, you will lead a team that connects product, strategy, and operations to measurable outcomes through analytics, machine learning, and artificial intelligence. You will tackle high-impact questions, build scalable capabilities, and help shape how the business prioritizes what matters most. If you are energized by ambiguity and motivated by real-world impact, you will thrive here. As a Data Science Lead in Wealth Management Decision Sciences, you will lead a team that delivers insights and machine learning solutions that improve client outcomes, product performance, and operational efficiency. You will partner closely with product, strategy, and operations teams to frame problems, define success measures, and deliver clear recommendations to senior stakeholders. You will help the team apply the right level of analytical rigor—ranging from descriptive analytics and visualization to advanced modeling—based on the business need. You will also identify and develop high-value opportunities to leverage artificial intelligence and agentic tools to elevate analytical productivity and decision quality. This is a vital hands-on role and manager role so we’re looking for exceptionally creative, ambitious, "data curious" thinkers who can help extract true business value from data. Job responsibilities Analyzing client usage across risk segments, both within and across products, in order to understand the relationship between a client’s risk appetite and their portfolio allocations Fine-tuning a machine learning model to predict the price elasticity of a client segment and inform policies around Advisor discounting Identifying the most common selling sequence by investment product and asset collection to understand key differences in purchasing behaviors across client segments and optimize Advisors’ sales practices Leveraging forecasting methods including regression analysis, year-over-year growth rates, as well as bottom-up and top-down approaches to estimate the potential impact of a product enhancement or pricing change Leading an ideation session with a product team and channel leaders to develop a go-to-market strategy for a new securities-based lending product pilot Delivering a simple, streamlined dashboard that tracks money movement across asset classes and investment products to help senior leaders understand potential shifts in market risk Required qualifications, capabilities, and skills BS Degree in an applicable STEM field and 5+ years of industry experience producing advanced analytics work. 2 years of people management experience, including performance management, coaching, and team development Think of yourself as part data scientist and part financial analyst with a passion for using analytics to make decisions that will drive impact for the firm and its clients Are an investments enthusiast: you have a solid understanding of asset allocation, investing and performance analytics concepts (e.g., separately managed accounts, margin lending, annuities, etc.); you are intrigued by the science behind portfolio construction; and you keep up with industry trends and financial news via sources like Bloomberg or Morningstar Think R is more than a letter, PYTHON is more than a snake, and Java is more than an island or coffee bean; you have programming skills needed for both the computational aspects of big data as well as working with statistical models Know when the “juice is worth the squeeze” – you stay abreast of new applications in machine learning, deep learning, and AI but can recognize and apply the right analytic approach when the challenge you are solving is less complex Have a strong presence, excellent presentation skills (both written and verbal), and low ego with the ability to deliver recommendations to multiple stakeholders and influence senior management Preferred qualifications, capabilities, and skills Experience supporting wealth management, investing, or self-directed investing businesses Demonstrated growth mindset through ongoing learning (for example: industry podcasts, staying current on new methods, or participation in data science competitions and conferences such as Knowledge Discovery and Data Mining)