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