Data Scientist, Senior Associate – Product, Experience and Technology (PXT) Analytics Team

JPMorgan Chase & Co.•New York, NY

About The Position

Help shape how Chase measures and improves developer productivity and technology efficiency. You will turn complex engineering and GenAI workflow data into trusted metrics, experiments, and insights that influence investment decisions and drive measurable outcomes. Join a team that partners closely with Product, Technology, and Finance to elevate the software development lifecycle and developer experience at scale. This role offers opportunities to grow your analytics engineering and modeling skills while working on high-impact, enterprise-wide initiatives. As a/an Data Scientist, Senior Associate on the Product, Experience and Technology Analytics team, you will build measurement frameworks that quantify improvements across CI/CD, GenAI-assisted development, and broader engineering initiatives. You will analyze large, complex datasets to identify drivers of delivery speed and engineering throughput. You will develop models and experimentation approaches to attribute impact and improve confidence in reported outcomes. You will create dashboards and reporting that help leaders make informed product and investment decisions. You will collaborate with partners across Product, Technology, and Finance to translate business questions into actionable analytics. Our team focuses on defining and scaling enterprise metrics that capture software delivery performance and developer experience. You will work with data from development pipelines, tooling adoption, and engineering activity signals to uncover trends and improvement opportunities. You will help operationalize data products and pipelines that support consistent reporting and repeatable analysis. You will contribute to best practices in developer productivity measurement and modern analytics engineering.

Requirements

Bachelor’s degree in Data Science, Statistics, Computer Science, or a related field.
4+ years of experience in data science, analytics, or a related role.
Demonstrated ability to define metrics and measurement frameworks in ambiguous or unstructured problem spaces.
Proficiency in analytics and visualization using tools such as SQL, Python, and Tableau (or equivalent).
Experience with modern data warehousing or lakehouse platforms (e.g., Snowflake, Databricks, Redshift).
Strong foundation in statistical methods, machine learning, and data mining techniques.
Proven ability to derive actionable insights from large, complex datasets.
Strong written, verbal, and presentation skills, with the ability to communicate to technical and non-technical audiences.
Ability to work effectively both independently and collaboratively in a fast-paced environment.

Nice To Haves

Master’s degree in Data Science, Statistics, Computer Science, or a related field.
Experience working in Agile environments and using Jira and Jira Align (or similar tools).
Familiarity with analytics engineering and orchestration frameworks such as dbt and Airflow (or equivalent).
Working knowledge of DevOps metrics and software development lifecycle measurement concepts.
Familiarity with AI-assisted coding tools (e.g., GitHub Copilot or similar).
Experience with interactive BI tools such as Looker or ThoughtSpot (or equivalent).
Experience mentoring junior data scientists or contributing to a collaborative, knowledge-sharing culture.

Responsibilities

Partner with product, engineering, and stakeholder teams to translate developer productivity and technology efficiency goals into measurable frameworks.
Analyze complex datasets (e.g., pipeline performance, developer activity signals, and GenAI tool usage) to identify trends, patterns, and improvement opportunities across the software development lifecycle.
Develop models to quantify the productivity and delivery impact of GenAI coding assistants, CI/CD improvements, and developer experience initiatives.
Design and run experiments to test hypotheses on tool adoption and workflow changes, and validate outcomes for accuracy and reliability.
Build and maintain dashboards, reports, and lightweight web experiences that surface key efficiency metrics (e.g., cycle time, throughput, and delivery performance).
Engineer scalable analytics pipelines that ensure reliable data flows from source systems to reporting and modeling layers.
Create clear, decision-ready narratives and recommendations for technical and non-technical audiences, including senior leaders.
Establish metric definitions, data quality checks, and documentation to support consistent interpretation and governance.
Collaborate with Technology and Finance partners to support impact attribution and investment measurement for major engineering initiatives.
Stay current on emerging practices in developer productivity measurement, GenAI-assisted development, and analytics engineering.