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# Quantitative Analytics & Model Analyst Senior - Data Operations and Machine Learning Operations in Cleveland

- **Company:** Energy Jobline
- **Location:** Pittsburgh, PA, United States
- **Experience:** Expert
- **Salary:** $86,250.0 - $172,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Confluence, JIRA, Microsoft Azure, Information Systems, Computer Programming, Continuous Integration, DevOps, Python (Programming Language), Machine Learning, OpenShift, Release Management, Standard Sql, DataOps, Software Deployment, Software Engineering, Systems Integration, Generative AI, Agentic-AI, Git, Pyspark, Information Technology, Deployment Automation, Data Analytics, Machine Learning Operations, Software Version Control, Docker, Jenkins
- **Published:** October 19, 2026
- **Apply:** https://www.energyjobline.com/job/quantitative-analytics-model-analyst-senior-data-operations-and-machine-learning-operations

## About the Role

n* Bachelor's degree in computer science, Information Systems, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field.\n
\n* Design and engineer AI / ML solutions, including ML models, GenAI applications, and agentic AI capabilities that address complex data science and business use cases\n
\n* Build scalable frameworks and reusable components for developing, integrating, testing, and deploying ML models and AI agents across the data science lifecycle\n
\n* Develop, validate, and test container images in OpenShift Container Platform (OCP) to ensure AI / ML models, agentic solutions, and supporting components can be reliably packaged and deployed across environments\n
\n* Experience supporting machine learning, analytics, software engineering, DevOps, or MLOps environments.\n
\n* Experience deploying and supporting analytical or machine learning solutions in enterprise environments.\n
\n* Programming/Coding experience in Python, R, or PySpark.\n
\n* Working knowledge of SQL, including the ability to understand, review, and manipulate SQL code.\n
\n* Experience with source control, CI/CD, and deployment tools, including Git, Jenkins, Docker, JIRA, or Confluence.\n
\n* Experience with Cloud Platforms: AWS or Azure\n
\n* Experience supporting code deployment and release management processes.\n
\n* Understanding of machine learning workflows and model deployment concepts\n
\n* Strong analytical and problem-solving skills.\n
\n* Excellent communication and presentation skills\n, Work ExperienceRoles at this level typically require a university / college degree, with 3+ years of relevant / direct industry experience. Certifications are often desired. In lieu of a degree, a comparable combination of education, job specific certification(s), and experience (including military service) may be considered.\n

## Description

At PNC, our people are our greatest differentiator and competitive advantage in the markets we serve. We are all united in delivering the best experience for our customers. We work together each day to foster an inclusive workplace culture where all of our employees feel respected, valued and have an opportunity to contribute to the company's success. As a Quantitative Analytics & Model Analyst Senior within PNC's Data Operations and Machine Learning Operations organization, you will be based in Pittsburgh, PA; Cleveland, OH; Washington, DC; East Brunswick, NJ; or Tyson's Corner, VA.\n, * Independently performs advanced quantitative analyses and model development to drive decision-making by running quantitative strategies. Makes recommendations based on analyses.
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 * Analyzes and develops new model frameworks by supporting the line of business. Refines, monitors, and reviews existing models. Conducts on-going communication with model owners and model developers during the course of the review. Works with larger, more complex datasets to create models.
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 * Performs quantitative analysis and develops complex reports. Performs qualitative and quantitative assessments of all aspects of models including theoretical aspects, model design and implementation as well as data quality and integrity. Analyzes complex data and associated quantitative analysis. Makes recommendations based on findings from data analytics.