The PXT Analytics Associate Data Scientist will play a key role in enhancing productivity and efficiency at Chase Bank. By providing business intelligence and analysis related to programs that enhance information technology efficiency, you will measure improvements to the software development lifecycle from various internal products and programs geared to improving the developer experience.
Job Description
The role involves sourcing, developing, and tracking metrics on initiatives related to CI/CD improvements as well as GenAI solutions integrated into the software development workflow. You will be responsible for creating analyses and insights in relation to company-wide efficiency metrics and attributing the impact of major engineering initiatives. Additionally, you will build analytics engineering pipelines and develop data science models to quantify that impact. You will work closely with cross-functional teams, including Technology and Finance, to analyze complex data sets, develop predictive models, and provide actionable insights that enhance our product offerings and technology efficiency.
Job Responsibilities:
- Collaborate with product managers, engineers, and other stakeholders to understand business objectives related to developer productivity and technology efficiency, and translate them into data-driven measurement frameworks.
- Analyze large and complex data sets – including CI/CD pipeline data, developer activity logs, and GenAI tool usage – to identify trends, patterns, and opportunities for improvement in the software development lifecycle.
- Build models to quantify the productivity impact of GenAI coding assistants, CI/CD pipeline improvements, and other developer experience initiatives on engineering output and delivery speed.
- Design and conduct experiments to test hypotheses around tool adoption and workflow changes, validating model outcomes to ensure accuracy and reliability.
- Create and maintain websites, dashboards, and reports that visualize key efficiency metrics – such as DORA metrics, cycle time, and developer throughput – facilitating informed decision-making across teams.
- Build and maintain analytics engineering pipelines to ensure reliable, scalable data flows from source systems to reporting and modeling layers.
- Stay current with emerging approaches in developer productivity measurement, GenAI-assisted development, and analytics engineering best practices.
Required Qualifications, Capabilities, and Skills:
- Bachelor's degree in Data Science, Statistics, Computer Science, or a related field.
- Proven experience (2+ years) in data science, analytics, or a related role, preferably within the financial services or technology industry.
- Comfort navigating ambiguous, unstructured problems — particularly in defining new metrics and measurement frameworks where established approaches may not yet exist.
- Experience with data analytics and/or visualization techniques (e.g., SQL, Python, Tableau), as well as data warehousing technologies (e.g., Snowflake, Databricks, Redshift).
- Solid foundation in machine learning techniques, statistical modeling, and data mining.
- Excellent problem-solving skills and the ability to work with complex data sets to derive actionable insights.
- Exceptional communication (written and verbal) and presentation skills, with the ability to convey findings and recommendations clearly to both technical and non-technical audiences, including senior leadership.
- Ability to work independently and collaboratively in a fast-paced, dynamic environment.
Preferred Qualifications, Capabilities, and Skills:
- Master's degree in Data Science, Statistics, Computer Science, or a related field.
- Experience with Agile methodologies and proficiency in project tracking tools such as Jira and Jira Align to manage workflows and enhance team collaboration.
- Familiarity with analytics engineering and orchestration frameworks (e.g., dbt, Airflow, or similar).
- Familiarity with DevOps metrics and a working understanding of 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), enhancing the ability to create intuitive and impactful data insights.
- Experience mentoring junior data scientists or contributing to the development of a collaborative, knowledge-sharing team culture.