Data Science Vice President - Wealth Management

JPMorgan Chase; New York (NY); On-site; USD 150,000 - 190,000; Full time

Job summary

JPMorgan Chase is seeking a data analytics leader to bridge product strategy and operations in wealth management. You will build scalable analytics and ML capabilities to drive measurable outcomes and inform business priorities for senior leadership.

The role emphasizes leading a team, collaborating with product, strategy, and analytics, and delivering decision-ready insights with real-world impact.

Responsibilities

• Analyzing client usage across risk segments to understand link between risk appetite and portfolio allocations.

Job description

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.

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)