Join JPMorganChase’s 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 initiative’s 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

• Master’s 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 impact—using 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