Data Scientist Manager, Wealth Management

Salary: $61,000 - 101,000 per year Requirements:

• Bachelors degree in an applicable STEM field and at least 5 years of industry experience delivering advanced analytics
• At least 2 years of people-management experience, including performance management, coaching, and team development
• Experience leading data science teams to deliver analytics and machine learning solutions that improve business performance
• Ability to select analytical methods suited to the complexity of a problem, from descriptive analytics and visualization to machine learning and AI-based decisioning
• Strong business judgment and critical thinking, including the ability to turn ambiguous questions into defined analytical problems and success measures
• Practical experience with statistical and quantitative techniques, such as decision trees, regression, multicollinearity analysis, string similarity, behavioral analytics, and look-alike modeling
• Ability to coach teams in big-data computation and statistical modeling, including work with complex SQL, PySpark, or Jupyter Notebooks
• Knowledge of developments in machine learning, deep learning, and AI, and the judgment to apply an appropriately simple or advanced approach
• Strong communication skills, with experience presenting insights, influencing stakeholders, and delivering recommendations in an executive-ready format
• Preferred: Experience supporting wealth management, investing, or self-directed investing businesses
• Preferred: A demonstrated commitment to ongoing learning, such as following industry developments, participating in data science competitions, or attending conferences

• Partner with product, strategy, and operations teams to support wealth management initiatives and decision-making with analytics
• Use statistical methods, data analysis, and AI-assisted approaches to address business questions involving products, finances, client engagement, and operations
• Lead analytics delivery from framing the problem and designing measurement through generating insights and presenting recommendations to senior stakeholders
• Develop and improve analytics, machine learning, and trigger capabilities that enhance operational processes in a scalable, sustainable way
• Analyze client usage across risk segments and products to understand connections between risk appetite and portfolio allocations
• Put experimentation frameworks into practice to assess enhancements, measure impact, and guide prioritization
• Evaluate client experiences and journey outcomes, including AI-enabled discovery, to identify improvements and measurable value
• Identify and develop valuable AI and agentic applications that increase analytical productivity and decision quality and improve portfolio allocations and customer outcomes
• Lead and develop a team delivering insights and machine learning solutions for client outcomes, product performance, and operational efficiency

• AI
• Support
• Jupyter
• Machine Learning
• PySpark
• SQL
• Big Data
• LESS

More:

We are partnering with hackajob to connect exceptional professionals with J.P. Morgan for a hands-on leadership role in Wealth Management Decision Sciences. You will help link product, strategy, and operations to measurable outcomes, addressing complex business questions and building scalable analytical capabilities. We are part of JPMorganChase, a global financial institution serving consumers, businesses, and institutional and government clients across banking, payments, and asset management. Our Consumer & Community Banking Data & Analytics organization brings together disciplines including data governance, strategy, reporting, data science, and machine learning, working closely with Technology. We offer competitive total rewards and, for eligible roles, incentive compensation, alongside benefits such as health coverage, retirement savings, backup childcare, tuition reimbursement, mental health support, and financial coaching. We value diversity and inclusion and provide reasonable accommodations for applicants and employees.

last updated 42 week of 2026