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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist, Senior Associate - Product, Experience and Technology (PXT) Analytics Team - **Company:** JPMorgan Chase & Co. - **Location:** Holtsville, NY, United States (Remote available) - **Experience:** Expert - **Salary:** $114,000.0 - $170,000.0 - **Contract:** Permanent contract - **Skills:** Agile Methodology, Artificial Intelligence, Airflow, Data Analysis, JIRA, Big Data, Continuous Integration, Data Mining, Data Warehousing, DevOps, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Software Engineering, SQL Databases, Data Streaming, Tableau (Software), GitHub Copilot, Delivery Pipeline, Snowflake, Kubernetes, Information Technology, Looker Analytics, Amazon Redshift, Databricks - **Published:** May 26, 2026 - **Apply:** https://www.careerjet.com/jobad/usa25bec35e0ddff4110e8ebb049499d26 ## About the Role * 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. Preferred qualifications, capabilities, and skills * 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. ## Description 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., * 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Collaboration Quantified: Lessons from Open Source Developer Networks](https://www.wearedevelopers.com/videos/1422-collaboration-quantified-lessons-from-open-source-developer-networks) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Data Analyst Salary in Switzerland](https://www.wearedevelopers.com/magazine/276-data-analyst-salary-in-switzerland) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)