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

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 analy...