Consumer Banking Data Scientist Director American Dream

Join JPMorganChases Consumer Banking Customer Data & Analytics team as a Data Scientist Director supporting the American Dream Initiative a firmwide multi-year effort to expand economic opportunity in communities across the United States. This is an exciting opportunity to help shape a high-profile strategic initiative by developing advanced analytics that drive customer engagement inform strategic decision-making and measure progress toward expanding economic opportunity and financial well-being across the country. This is a senior leadership role that requires technical expertise strategic vision and executive-level influence to translate complex data into clear actionable decisions.

As a Data Scientist Director at JPMorganChase within the Consumer Banking Customer Data & Analytics team you will build and lead a high-performing team responsible for the end-to-end delivery of analytics solutions that support customer acquisition engagement servicing and retention in support of the American Dream Initiative. You will define the analytics strategy develop a high-performing team and partner closely with stakeholders across the firm to transform data-driven insights into measurable business and community outcomes.

Job responsibilities

• Define and drive the customer analytics strategy for the American Dream Initiative aligning measurement and prioritization to core goals.
• Build and lead a team to deliver high-impact insights across the customer lifecycle performance and program effectiveness.
• Establish and oversee a performance measurement framework including KPI development executive reporting experimentation and impact assessment to drive data-informed decision-making and program optimization.
• Partner with Product Marketing Research and business stakeholders to develop data-driven customer engagement strategies that improve acquisition deepening and retention.
• Leverage emerging AI machine learning and analytics capabilities to develop innovative solutions that enhance customer outcomes and advance the initiatives goals.
• Translate complex analyses into clear narratives and recommendations for senior leaders balancing strategic ambition with operational feasibility risk discipline and measurable outcomes.

Required qualifications capabilities and skills

• Masters or PhD in a quantitative field and 10 years of analytics experience.
• Proven senior leadership experience managing and developing multidisciplinary analytics teams in a large enterprise environment.
• Deep expertise in data science and analytics including hands-on experience with predictive modeling statistical analysis segmentation and experimentation.
• Demonstrated ability to deliver AI machine learning and analytics solutions that drive measurable business outcomes ideally within financial services or consumer products.
• Strong business acumen with the ability to frame analytical problems in terms of business strategy and translate insights into executive-level recommendations.
• Experience leading analytics across multiple concurrent business domains balancing near-term delivery with longer-term capability building.
• Exceptional stakeholder management and communication skills with a track record of influencing senior leaders and cross-functional partners.
• Proficiency in Python and/or R and experience with modern data platforms such as Snowflake or Databricks.
• Strong project and program management skills including the ability to define success metrics manage risks and drive execution across complex initiatives.

Preferred qualifications capabilities and skills

• Consumer banking/financial services analytics across customer lifecycle channels marketing effectiveness and servicing strategy
• Interest/experience in economic opportunity community development financial health or social impactusing data to drive measurable outcomes
• Building KPI frameworks and multi-channel attribution for initiatives balancing growth and customer impact objectives
• Hands-on generative AI experience (LLMs retrieval-augmented generation agentic AI frameworks)
• Causal inference and experimentation at scale (A/B testing robust testing frameworks)
• Responsible AI knowledge: model governance and regulatory considerations in financial services
• Driving analytics adoption via change management self-service tooling organizational enablement; familiarity with Agile/product practices