VP Data Scientist Lead - Product, Experience and Technology (PXT) Analytics Team

About the role

Join us to shape how we measure and improve the software delivery experience across Chase. You will lead a team that turns product and engineering data into clear, trusted insights that influence strategy, investment decisions, and roadmaps. This role offers the opportunity to define measurement frameworks in an area where standards are still evolving, and to build a culture of analytical rigor and practical impact. If you enjoy ambiguous problems, influencing senior stakeholders, and developing talent, you will thrive with us. Job summary As a Vice President, Data Science Lead in the Product, Experience and Technology Analytics team, you will define and execute the analytics strategy that measures developer productivity, technology efficiency, and product value across internal platforms and tools. You will lead data scientists and analysts to build measurement frameworks, dashboards, and models that quantify adoption, engagement, and outcomes across the software development lifecycle. You will partner closely with leaders across Product, Technology, and Finance to translate complex findings into clear narratives that drive decisions. You will set priorities, raise the bar on analytical rigor, and help your team deliver high-impact insights at scale. Our analytics work focuses on initiatives that improve software delivery and developer workflows, including measurement of CI/CD enhancements and adoption and impact of generative AI solutions embedded in engineering processes. You will help establish consistent definitions and reporting for metrics such as cycle time, throughput, and product adoption funnels, enabling leaders to understand what is working and where to invest next. Job responsibilities Lead, manage, and develop a team of data scientists and analysts through goal setting, coaching, feedback, and performance management. Define and own measurement frameworks that quantify adoption, engagement, feature effectiveness, and value delivery across internal developer platforms and tools. Partner with senior product and engineering leaders to align analytics priorities with developer productivity and technology efficiency objectives. Analyze large, complex datasets (for example, pipeline events, activity logs, and usage telemetry) to identify trends and opportunities across the software development lifecycle. Direct the development of models that quantify the impact of workflow automation and generative AI tools on delivery speed and engineering outcomes. J Guide experiment design and evaluation to test hypotheses on adoption, feature changes, and workflow improvements, ensuring results are methodologically sound. Oversee dashboards and recurring reporting that make key metrics easy to understand and actionable for leaders and teams. Drive scalable analytics engineering pipelines and dependable data flows from source systems to reporting and modeling layers. Communicate analytical findings as clear, decision-ready recommendations for senior stakeholders across Product, Technology, and Finance. Set standards for analytical rigor, documentation, and reusable assets, improving consistency across workstreams. Stay current on product analytics and developer productivity measurement practices, bringing new approaches into the team's work. Required qualifications, capabilities, and skills Bachelor's degree in data science, statistics, computer science, or a related field. Six or more years of experience in data science, product analytics, or a related analytics role. Two or more years of people management experience, including coaching, performance management, and team development. Demonstrated ability to define metrics and measurement frameworks for product adoption, engagement, and value. Proven ability to structure ambiguous problems and deliver clear analytical approaches and outputs. Strong proficiency in SQL and Python for analysis, and experience with data visualization tools used for executive reporting. Experience working with modern data warehouse or lakehouse technologies. Strong foundation in machine learning, statistical modeling, and data mining techniques. Excellent communication and presentation skills, including ability to influence technical and non-technical senior stakeholders. Demonstrated ability to manage competing priorities and deliver results in a fast-paced environment. Preferred qualifications, capabilities, and skills Master's degree or PhD in a quantitative field. Experience leading analytics for platform, infrastructure, or internal tooling products. Familiarity with Agile delivery practices and common work management tools used to run sprints and track delivery. Experience with analytics engineering or orchestration frameworks (for example, dbt or Airflow). Working knowledge of software delivery lifecycle concepts and related operational metrics. Familiarity with AI-assisted development tools used to support coding and delivery workflows. Experience building or scaling an