Data Scientist Lead-Vice President

JPMorgan Chase & Co.; New York (NY); On-site; USD 120,000 - 190,000; Full time

Job summary

JPMorgan Chase & Co. in New York is seeking a Data Science Lead in Wealth Management Decision Sciences to guide analytics and ML initiatives that boost client outcomes and operational efficiency.

You will frame problems with product, strategy, and operations, deliver actionable recommendations to senior leaders, and drive scalable analytics across risk, portfolio, and client engagement using descriptive to advanced modeling.

Qualifications

• BS Degree in an applicable STEM field; 5+ years of analytics experience are expected.
• 2 years of people management experience including coaching and team development.
• Led teams of data scientists to deliver analytics and ML solutions that improve business performance.
• Right-size analytical approaches from descriptive analytics to ML/AI-based decisioning.
• Strong business acumen and ability to translate ambiguous questions into well-defined problems and metrics.
• Hands-on experience with statistical techniques (e.g., decision trees, regression, look-alike modeling).
• Coach a team on computational aspects of big data and statistical models while solving problems, not just boosting model curves.
• Stay current with ML/AI applications and apply the right approach for each challenge.
• Strong communication skills with executive-ready storytelling.

Responsibilities

• Collaborate with product, strategy, and operations teams to provide analytical support for wealth management initiatives and decision-making.
• Apply statistical methods and AI-assisted approaches to solve business problems across product, financial, client engagement, and operations outcomes.
• Lead end-to-end analytics delivery from problem framing and measurement design to insight communication to senior stakeholders.
• Develop analytics approaches for ML and trigger capabilities to improve operations in a scalable, sustainable way.
• Analyze client usage across risk segments to understand relationships between risk appetite and portfolio allocations.
• Operationalize experimentation frameworks to evaluate enhancements, quantify impact, and inform prioritization.
• Assess client experience and journey outcomes, including AI-enabled discovery, to identify improvement opportunities and measurable value creation.
• Identify and develop high-value AI and agentic use cases to improve analytical productivity and customer outcomes.

Skills

Leadership

Statistical analysis

Machine learning

Communication

SQL / PySpark

Problem framing

Visualization

Education

BS Degree in STEM

Tools

SQL

PySpark

Jupyter Notebooks

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. 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 aData Science LeadinWealth 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

• Collaborate with product, strategy, and operations teams to provide analytical support for wealth management initiatives and decision-making
• Apply statistical methods, data analysis, and artificial intelligence-assisted approaches to solve business problems across product, financial, client engagement, and operational outcomes
• Lead end-to-end analytics delivery, from problem framing and measurement design through insight generation and communication to senior stakeholders
• Develop and refine analytics approaches for machine learning and trigger capabilities thatimprove the operations process in a scalable, sustainable way
• Analyze client usage across risk segments and products to understand relationships between risk appetite and portfolio allocations
• Operationalize experimentation frameworks to evaluate enhancements, quantify impact, and inform prioritization
• Assess client experience and journey outcomes, including artificial intelligence-enabled discovery, to identify improvement opportunities and measurable value creation
• Identify and develop high-value artificial intelligence and agentic use cases that improve analytical productivity, decision quality, portfolio allocations, and customer outcomes

Required qualifications, capabilities, and skills

• BS Degree in an applicable STEM field and 5+ years of industry experience producing advanced analytics work.

• 2 yearsof people management experience, including performance management, coaching, and team development
• Demonstrated experience leading teams of data scientists to deliver analytics and machine learning solutions that improve business performance
• Ability to right-size analytical approaches to problem complexity, from descriptive analytics and visualization through machine learning and artificial intelligence-based decisioning
• Strong business acumen and critical thinking, with the ability to translate ambiguous questions into well-defined analytical problem statements and success metrics
• Hands-on experience applying statistical and quantitative techniques (for example: decision trees, linear regression, logistic regression, ridge regression, multicollinearity analysis, string similarity, behavioral analytics, and look-alike modeling)
• Effectively coach a team on both the computational aspects of big data as well as working with statistical models (e.g., complex SQL scripts, PySpark libraries or Jupyter Notebooks) while staying focused on solving problems, not just boosting model performance curves
• 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
• Strong communication skills, with experience synthesizing insights and influencing stakeholders through clear recommendations and executive-ready storytelling

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)