Job Description
Help shape how Chase improves product development productivity as AI transforms the way Product, Experience, Technology, and Data & Analytics teams work. You will lead a Strategic Insights team that rapidly tests hypotheses, identifies effective operating practices and AI-enabled workflows, and translates evidence into clear actions for Product Operations and Finance leaders.
As a Data Science Lead within Product, Experience, and Technology (PXT) Data and Analytics at JPMorganChase, you will lead high-velocity analysis of operating-model changes, product practices, and AI tools across the product development lifecycle. You will turn ambiguous productivity questions into decision-ready findings and recommendations, partner with execution teams to assign actions and timelines, and track adoption and outcomes.
Job responsibilities
Lead high-velocity, hypothesis-driven analysis of Product Operations and product development productivity
Turn ambiguous questions about operating models, workflows, and AI adoption into focused analytical plans and decision criteria
Assess how AI tools and ways of working affect delivery speed, quality, capacity, and value
Combine product, delivery, and AI adoption data with stakeholder context to identify actionable opportunities
Produce decision-ready briefs with clear findings, recommendations, owners, timelines, benefits, and guardrails
Partner across Product Operations, Product, Technology, and Finance to implement recommendations and track adoption and outcomes
Set standards for analytical quality, productivity measurement, executive communication, and responsible use of data and AI
Develop and manage a high-performing team and influence senior leaders with clear, practical insights
Required qualifications, capabilities and skills
Bachelor’s degree in quantitative discipline and 5+ years of applied data science or analytics experience
Experience leading analytical work from problem framing through implementation and outcome measurement
Strong hypothesis development, experimental design, quantitative analysis, synthesis, and problem-solving skills
Proficiency with Python, SQL, large datasets, modern analytics platforms, and visualization tools
Understanding of product development workflows and productivity metrics, including cycle time, throughput, quality, capacity, and adoption
Ability to work at pace in ambiguous environments, balancing analytical rigor with practical decision-making
Proven ability to influence senior stakeholders, lead analytical talent, and translate complex findings into clear actions and measurable outcomes
Preferred qualifications, capabilities and skills
Experience building or leading a consulting-style strategic analytics or insights team
Experience in product operations, product management, technology delivery, organizational effectiveness, or financial services
Experience evaluating operating-model changes, workflow redesign, or enterprise productivity initiatives
Experience with experimentation, causal inference, forecasting, optimization, machine learning, or natural language processing
Experience evaluating or deploying Generative AI and agentic tools in operational workflows, including adoption, telemetry, quality, risk, cost, and value realization
Familiarity with product development lifecycle data and tools used to manage requirements, backlogs, dependencies, code, testing, and delivery
Demonstrated success creating executive briefs and operating mechanisms that drive decisions, ownership, adoption, and follow-through
Master’s degree, MBA, PhD, or equivalent advanced degree
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