Principle Software Engineer Global Technology Analytics, Insights and Metrics

If you are looking for a game-changing career working for one of the worlds leading financial institutions youve come to the right place.

As a Principle Software Engineer at JPMorganChase within the Global Technology - Analytics Insights and Measurements (GT AIM) team you will deliver trusted decision-grade insight across GT through rigorous statistical analysis and domain-informed interpretation. You will be entrusted in delivering market-leading technology products in a secure stable and scalable way. As a core technical contributor you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firms business objectives.

Reporting to the Head of GT Architecture and Strategy (GTAS) this role applies sound statistical and analytical methods to technology data to inform strategy execution and investment decisions across multiple technology domains. The role works in close partnership with leaders of strategic programs providing continuous statistical analysis and insight to support priority outcomes.

The role requires deep understanding of software engineering delivery models and flows e.g. feature branch trunk-based and integrated delivery to ensure metrics and analysis accurately reflect how technology is delivered. Areas of focus include developer productivity delivery and portfolio performance technology spend and value realization return on investment and the adoption and impact of Artificial Intelligence across GT.

The emphasis is on building internally owned transparent and explainable analytics through sound statistical methods rather than relying on opaque third-party tools. All roles are hands-on. Managers provide leadership and direction while actively contributing to analysis and insight delivery. Senior Individual Contributors independently own complex analytical problems and influence outcomes through expertise and insight.

We have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.

Insights Communications and Reporting

• Define create deliver establish and maintain a metrics framework and complementary visuals aligned to CTO and technology leadership decision needs. Your framework will be inclusive of many different technology initiatives including emerging capabilities such as Artificial Intelligence (AI) Software Engineering Portfolio Management and more.
• Build strong relationships across various GT functions. Communicate statistical findings effectively to technical and non-technical audiences without oversimplification or false precision. Narratives and analyses need to be clear. They need to articulate what is happening why it is happening and how confident the conclusions are.
• Work closely to JPMC key strategic programs and initiatives while providing continuous analysis & insights to support their priority outcomes all with sound statistical measures. Your insights must explain performance trends variability and drivers across all of GT.
• Lead coach and develop a small team of highly skilled impactful analytics professionals. Manage corresponding standards for statistical rigor transparency and clarity.

Statistical Analysis and Data Interpretation

• Continuously refine analytical approaches as technology strategy architecture and delivery practices evolve. Support technology leadership in understanding trade-offs risks opportunities and uncertainty.
• Conclusions provided must be sound statistically and contextually valid and based on actual engineering and business ecosystems. Collaborate closely with engineering platform architecture and AI enablement teams to understand delivery practices workflows and constraints
• Perform hands-on statistical analysis using appropriate descriptive inferential and exploratory techniques. Apply those techniques and reasoning to assess variability confidence uncertainty statistical significance and margin of error where appropriate.
• Evaluate distributions trends and changes over time while accounting for structural differences in teams systems and delivery models. Be able to distinguish correlation from causation and clearly communicate analytical limitations assumptions and confidence levels.

Operations Measurements and Instrumentation

• Identify required data points needed to answer key analytical and statistical questions then define requirements for instrumenting data at the source.
• Ensure metrics are compatible with different engineering flows including feature branch development trunk-based development and integrated delivery.
• Improve data quality consistency and traceability over time. Maintain clear documentation of metric definitions statistical methods and calculation logic.
• Ensure reporting supports informed decision-making rather than metric consumption without context.

Required qualifications capabilities and skills:

• Degree in Mathematics Statistics Data Science Engineering Computer Science or equivalent 5 years applicable work experience.
• 3 years experience performing statistical analytics data science or performance measurement roles.
• Practical experience working with technology delivery portfolio financial or AI-related data.
• Demonstrated experience applying statistical methods to real-world imperfect datasets and evolving delivery practices.
• Strong familiarity with concepts such as statistical significance confidence intervals variability and margin of error and when their use is appropriate.
• Proficiencies in a modern data stack. This includes Excel Python R Studio Power BI Tableau Qlik SQL Python dbt Databricks Snowflake and Microsoft Fabric alongside specialized portfolio and spend analytics tools like Apptio.
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g. cloud artificial intelligence machine learning mobile etc.)
• Experience influencing senior technology leaders and guiding decision-making

Preferred qualifications capabilities and skills

• Desire and ability to mentor peers through statistical expertise and engineering domain knowledge.
• Strong formal training in statistics.
• Intellectual curiosity and commitment to statistical rigor.
• Respect for the complexity and variability of software delivery systems within a large enterprise.
• Practical cloud native experience.
• Proficiency in automation and continuous delivery methods (CI/CD pipelines).
• Practical understanding of software engineering delivery models including but not limited to feature branch trunk-based and integrated delivery.
• Experience leading or mentoring analytics professionals.