Quantitative Analytics & Model Consultant – Data Operations, Machine Learning Operations

PNC

. Design, develop, and support scalable data, AI, and machine learning solutions in development and production environments .

Posted 10/6/2026full-timeUnited StatesMid-LevelSenior💰 $91,000 - $202,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying scalable data and AI solutions, with a strong focus on machine learning workflows, CI/CD pipelines, and model monitoring. Proficient in collaborating with cross-functional teams to drive business value through data-driven decision-making.

Highest-signal resume keywords

Machine Learning DeploymentCI/CD Pipeline DevelopmentPython ProgrammingAWS or Azure ExperienceData Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData Pipeline DevelopmentModel MonitoringQuantitative AnalysisStatistical AnalysisModel ValidationData Quality AssessmentFeature EngineeringModel GovernanceAnalytical Model Development

Soft Skills

Strong Communication SkillsProblem-Solving SkillsStakeholder EngagementCollaboration SkillsRelationship Management

Tools & Technologies

AWSAzurePythonSQLRPySpark

Industry Keywords

Data ScienceMLOpsDevOpsBankingFinancial ServicesRisk Management

Tech Stack

Tools & technologies

AWSAzurePySparkPythonSQL

About the role

Key responsibilities & impact

Design, develop, and support scalable data, AI, and machine learning solutions in development and production environments
Move machine learning and analytical models from development into production, including packaging, integration, testing, and deployment
Build and maintain automated, reusable CI/CD pipelines for model training, scoring, deployment, and retraining
Develop scalable data pipelines and reusable data and feature products
Develop model monitoring and analytics frameworks tracking performance, stability, and data drift
Design and build AI agents and agentic workflows using LLMs, including prompt design, RAG, tool/API integration, and orchestration
Develop testing and evaluation frameworks for AI agents covering accuracy, consistency, hallucinations, bias, security risks, and UAT
Deploy AI agents into production with guardrails, human-in-the-loop controls, and logging
Monitor AI agent quality, usage, and cost over time
Measure business value by defining success metrics, setting baselines, and tracking outcomes such as lift, loss reduction, and efficiency gains
Partner with data scientists, model owners, technology teams, business stakeholders, and lines of business
Perform complex quantitative analyses and model development to support decision-making
Develop model frameworks, refine and monitor existing models, and validate models
Conduct ongoing communication with model owners and developers during reviews
Perform qualitative and quantitative assessments of model theory, design, implementation, data quality, and integrity
Evaluate model risks and conclude on model strengths and limitations
Prepare and analyze documentation for validation and regulatory compliance

Requirements

What you’ll need

Bachelor’s degree in data science, Engineering, Mathematics, Statistics, or a related quantitative field
Experience supporting machine learning, analytics, DevOps, or MLOps environments
Experience deploying and supporting analytical or machine learning solutions in enterprise environments
Programming/coding experience in Python, SQL, R, or PySpark
Experience with AWS or Azure
Understanding of machine learning workflows and model deployment concepts
Strong analytical and problem-solving skills
Strong 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
5+ years of industry-relevant experience typically required at this level
In lieu of a degree, a comparable combination of education, job-specific certification(s), and experience may be considered
No required certifications
No required licenses
PNC will not provide sponsorship for employment visas or participate in STEM OPT

Benefits

Comp & perks

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
Incentive-eligible compensation based on company, business, and/or individual performance