Senior Quantitative Analytics and Model Analyst – Data Operations, Machine Learning Operations

PNC

. Design, engineer, deploy, and support scalable AI and machine learning solutions for enterprise analytics .

Posted 9/17/2026full-timeTysons Corner • Ohio • United StatesSenior💰 $86,250 - $172,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing, deploying, and supporting scalable AI and machine learning solutions, with a strong focus on model lifecycle management and collaboration across technical and business teams. Proficient in programming languages such as Python and R, and experienced in cloud platforms like AWS or Azure.

Highest-signal resume keywords

Machine Learning Solutions DeploymentPython ProgrammingSQL ProficiencyCloud Platforms (AWS/Azure)Model Lifecycle Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel DevelopmentQuantitative AnalysisData AnalysisModel ValidationModel MonitoringContainerization (OpenShift)CI/CD ProcessesVersion Control (Git)Data Engineering

Soft Skills

Analytical SkillsProblem-Solving SkillsCommunication SkillsCollaboration SkillsStakeholder Engagement

Tools & Technologies

JenkinsDockerJIRAConfluenceOpenShift Container Platform

Industry Keywords

BankingFinancial ServicesRisk ManagementModel GovernanceEnterprise Data Ecosystems

Tech Stack

Tools & technologies

AWSAzureCloudDockerJenkinsOpenShiftPySparkPythonSQL

About the role

Key responsibilities & impact

Design, engineer, deploy, and support scalable AI and machine learning solutions for enterprise analytics
Build scalable frameworks and reusable components for model development, integration, testing, deployment, and monitoring
Collaborate with data scientists to operate analytical and machine learning solutions in production
Support model lifecycle management, including deployment, testing, validation, performance monitoring, and ongoing optimization
Manage model releases, version control, and deployment processes
Partner with engineering teams to establish infrastructure standards for model deployment and operational support
Support automation, orchestration, and infrastructure-as-code initiatives
Troubleshoot deployment, integration, and operational issues across AI/ML ecosystems
Partner with business leaders, technology teams, data scientists, and lines of business to understand requirements and deliver solutions
Independently perform advanced quantitative analyses and model development to support decision-making
Analyze and develop model frameworks; refine, monitor, and review existing models
Communicate with model owners and model developers during reviews
Work with large and complex datasets to create models
Perform quantitative analysis and develop complex reports
Assess models’ theoretical aspects, design, implementation, data quality, and integrity
Measure and analyze model risks and identify model strengths and limitations
Prepare and analyze documentation for validation and regulatory compliance

Requirements

What you’ll need

Bachelor’s degree in computer science, Information Systems, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field
3+ years of relevant/direct industry experience typically required for this level
Experience supporting machine learning, analytics, software engineering, DevOps, or MLOps environments
Experience deploying and supporting analytical or machine learning solutions in enterprise environments
Programming/coding experience in Python, R, or PySpark
Working knowledge of SQL, including understanding, reviewing, and manipulating SQL code
Experience with source control, CI/CD, and deployment tools, including Git, Jenkins, Docker, JIRA, or Confluence
Experience with cloud platforms: AWS or Azure
Experience supporting code deployment and release management processes
Understanding of machine learning workflows and model deployment concepts
Experience developing, validating, and testing container images in OpenShift Container Platform (OCP)
Experience designing and engineering AI/ML solutions, including ML models, GenAI applications, and agentic AI capabilities
Experience building scalable frameworks and reusable components across the data science lifecycle
Strong analytical and problem-solving skills
Excellent communication and presentation skills
Ability to influence and collaborate across technical and business teams
Experience working in highly collaborative, cross-functional environments
Strong stakeholder engagement and relationship management capabilities
Banking, financial services, lending, or risk management experience preferred
Familiarity with model governance, model monitoring, and production support processes preferred
Understanding of data engineering and enterprise data ecosystems preferred
No required certifications or licenses
PNC will not provide sponsorship for employment visas or participate in STEM OPT for this position

Benefits

Comp & perks

Incentive-eligible compensation based on company, business, and/or individual performance
Medical/prescription drug coverage with a Health Savings Account feature
Dental and vision options
Employee and spouse/child life insurance
Short- and long-term disability protection
401(k) with PNC match
Pension plan
Stock purchase plan
Dependent care reimbursement account
Back-up child/elder care
Adoption, surrogacy, and doula reimbursement
Educational assistance, including select programs fully paid
Robust wellness program with financial incentives
Maternity and/or parental leave
Up to 11 paid holidays each year
9 occasional absence days each year
15 to 25 vacation days each year, depending on career level