Data Science Vice President - Wealth Management

JPMorgan Chase & Co.; Columbus (OH); On-site; USD 140,000 - 210,000; Full time

Job summary

JPMorgan Chase & Co. in Columbus, OH, seeks a Data Science Lead within Wealth Management Decision Sciences to guide a team that translates analytics into actionable client and product outcomes.

You will collaborate across product, strategy, and operations to frame problems, design experiments, and deploy ML solutions to improve client outcomes and efficiency. You will partner with leadership to frame success metrics, deliver actionable recommendations, and mentor team members toward growth

Qualifications

• Bachelor’s degree in a STEM field.
• 5+ years of analytics or data science experience in a business setting.
• 2+ years of people management experience, including coaching and performance management.
• Experience delivering analytics and ML solutions that improve business outcomes.
• Ability to translate ambiguous business questions into defined analytical problems and success metrics.
• Hands-on with statistical and quantitative techniques (e.g., regression, classification, feature selection).
• Proficiency with SQL and Python and handling large datasets.
• Strong communication of analytic findings to senior stakeholders.

Responsibilities

• Partner with product, strategy, and operations teams to identify high-impact opportunities and define measurable outcomes.
• Lead end-to-end analytics delivery, including framing, measurement design, insights, and executive storytelling.
• Build and scale ML solutions to improve client outcomes, product performance, and efficiency.
• Develop modeling and trigger capabilities to improve operations sustainably.
• Analyze client journeys to identify drivers of engagement and outcomes.
• Design experimentation and test‑and‑learn frameworks to quantify impact.
• Evaluate journeys to identify improvement opportunities and value creation metrics.
• Identify AI use cases that elevate productivity and decision quality.
• Coach and develop team members with clear standards and feedback.

Skills

Leadership

Analytics

Problem framing

Storytelling

Experimentation frameworks

Statistical techniques

Communication with stakeholders

Mentoring/people management

Education

Bachelor’s degree in STEM

Master’s or PhD in quantitative field

Tools

SQL

Python

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. If you are energized by ambiguity and motivated by real-world impact, you will thrive here.

As a Data Science Lead at JPMorganChase within Wealth Management Decision Sciences, you will lead a team that delivers insights and machine learning solutions to improve client outcomes, product performance, and operational efficiency. You will partner with product, strategy, and operations leaders to frame problems, define success measures, and deliver recommendations that influence prioritization and investment decisions. You will apply the right level of analytical rigor—from descriptive analytics and visualization to advanced modeling—based on the business need, while remaining hands‑on in delivery.

Job responsibilities

• Partner with product, strategy, and operations teams to identify high-impact opportunities and define measurable outcomes
• Lead end-to-end analytics delivery, including problem framing, measurement design, insight generation, and executive‑ready storytelling
• Build and scale machine learning solutions that improve client outcomes, product performance, and operational efficiency
• Develop and refine modeling and trigger capabilities that improve operational processes in a sustainable, scalable way
• Analyze client behavior across segments and products to identify drivers of engagement, outcomes, and portfolio allocation patterns
• Design and operationalize experimentation and test‑and‑learn frameworks to quantify impact and inform prioritization
• Evaluate client journeys and experience outcomes to identify improvement opportunities and value creation metrics
• Identify and develop high‑value artificial intelligence and agentic use cases that elevate analytical productivity and decision quality
• Coach and develop team members through clear standards, feedback, performance management, and career growth support

Required qualifications, capabilities and skills

• Bachelor’s degree in a STEM‑related field
• 5+ years of industry experience delivering advanced analytics or data science solutions in a business setting
• 2+ years of people management experience, including coaching, performance management, and team development
• Demonstrated experience leading teams to deliver analytics and machine learning solutions that improve measurable business outcomes
• Demonstrated ability to translate ambiguous business questions into well‑defined analytical problem statements and success metrics
• Hands‑on experience applying statistical and quantitative techniques (for example, linear regression, logistic regression, decision trees, regularization methods, and feature selection/diagnostics)
• Proficiency in developing analyses using modern data tools and programming (for example, SQL and Python) and working with large datasets
• Experience communicating complex analytical findings to senior stakeholders through clear recommendations and structured narratives
• Demonstrated ability to select appropriately simple or advanced methods based on problem complexity and expected business value

Preferred qualifications, capabilities and skills

• Experience supporting wealth management, investing, or self‑directed investing businesses
• Master’s degree or PhD in a quantitative field (for example, statistics, computer science, economics, or applied mathematics)
• Experience building and scaling experimentation programs (for example, A/B testing, causal measurement approaches, or quasi‑experimental design)
• Demonstrated continuous learning in data science and applied machine learning (for example, courses, conferences, or competitions)