The role
From JPMorgan Chase's own posting.
Build a career where your analyses directly improve how software gets delivered at scale. In this role, you’ll help teams understand what works, what doesn’t, and where investments meaningfully increase engineering efficiency. You’ll turn complex engineering and finance data into clear, actionable insights leaders can use. You’ll also help shape modern measurement approaches for emerging tools and ways of working.
Job summary
As a Senior Associate, Data Scientist in People Analytics, you will help measure and improve developer productivity and technology efficiency across major engineering initiatives. You’ll source, develop, and track metrics tied to software delivery performance, including continuous integration/continuous delivery (CI/CD) improvements and generative AI solutions embedded in the development workflow. You’ll build analytical models and scalable data pipelines to quantify impact and explain drivers of change. You’ll partner closely with cross-functional teams in Technology and Finance to translate complex data into decisions that improve outcomes for our teams and our customers.
You’ll work with diverse data sources such as CI/CD pipeline telemetry, developer activity signals, and tool usage patterns to identify trends, quantify adoption, and highlight opportunities to streamline delivery. Your work will support enterprise-wide measurement frameworks and help establish consistent, trusted metrics that enable teams to benchmark progress over time.
Job responsibilities
Collaborate with product managers, engineers, and stakeholders to translate business objectives into measurable productivity and efficiency frameworks
Analyze large, complex data sets (e.g., CI/CD pipeline data, developer activity logs, and AI tool usage) to identify trends, bottlenecks, and improvement opportunities
Build models that quantify the productivity impact of AI-assisted development, CI/CD enhancements, and other developer experience initiatives
Design and run experiments to test hypotheses on tool adoption and workflow changes, validating outcomes for accuracy and reliability
Create and maintain dashboards, reports, and web-based views of key efficiency metrics (e.g., cycle time, throughput, and operational performance indicators)
Build and maintain analytics engineering pipelines to deliver reliable, scalable data for reporting and modeling
Stay current on best practices in developer productivity measurement, AI-assisted development, and modern analytics engineering
Required qualifications, capabilities, and skills
Bachelor’s degree in Mathematics, Data Science, Statistics, Computer Science, or a related field
Four years of experience in data science, analytics, or a related role
Demonstrated ability to define new metrics and measurement frameworks in ambiguous problem spaces
Proficiency in data analysis and visualization (e.g., SQL, Python, Tableau or similar tools)
Experience with data warehousing and analytics platforms (e.g., Snowflake, Databricks, Amazon Redshift, or similar technologies)
Strong foundation in machine learning, statistical modeling, and data mining
Strong problem-solving skills and ability to derive actionable insights from complex data sets
Excellent written, verbal, and presentation communication skills, including the ability to explain findings to technical and non-technical audiences
Preferred qualifications, capabilities, and skills
Master’s degree in Mathematics, Data Science, Statistics, Computer Science, or a related field
Experience working in Agile environments and using project tracking tools (e.g., Jira and Jira Align)
Familiarity with analytics engineering and orchestration tools (e.g., dbt, Apache Airflow, or similar)
Familiarity with software delivery metrics and the software development lifecycle
Familiarity with AI-assisted coding tools (e.g., GitHub Copilot, Claude Code, or similar)
Experience with interactive data visualization platforms (e.g., ThoughtSpot, Looker, or similar)
Experience mentoring junior data scientists or contributing to a collaborative, knowledge-sharing culture