Quantitative Analytics & Model Consultant - Data Operations and Machine Learning Operations

Job Profile

Position Overview

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 Consultant within PNC's Data Operations and Machine Learning Operations organization, you will be based in Pittsburgh, PA; Cleveland, OH; or Tyson's Corner, VA.

Position Overview

PNC is seeking a Quantitative Analytics & Model Development Consultant to join our Machine Learning Operationalization (MLOps) team. This role is responsible for designing, developing, and supporting scalable Data, AI and machine learning solutions that enable advanced analytics across the enterprise.

The successful candidate will combine expertise in data engineering, cloud infrastructure, DevOps, and MLOps to help support and design analytical and machine learning solutions in development and production environments. This individual will partner closely with data scientists, technology teams, business stakeholders, and lines of business to ensure solutions are reliable, scalable, secure, and operationally efficient.

Key

Responsibilities

• Model Deployment: Partner with data scientists to move machine learning and analytical models from development into production, including packaging, integration, testing, and deployment.
• ML Pipelines and Automation: Build and maintain automated, reusable pipelines (CI/CD) for model training, scoring, deployment, and retraining, using cloud and infrastructure-as-code tools where applicable.
• Data and Feature Engineering: Develop scalable data pipelines and reusable data and feature products that provide reliable, high-quality inputs to models.
• Model Monitoring and Analytics: Develop model monitoring and analytic frameworks that can be scaled to support many models, tracking performance, stability, data drift.
• AI Agent Development: Design and build AI agents and agentic workflows using large language models (LLMs), including prompt design, retrieval-augmented generation (RAG), tool and API integration, and orchestration.
• AI Agent Testing and Evaluation: Develop testing and evaluation frameworks for AI agents covering accuracy, consistency, hallucinations, bias, and security risks (e.g., prompt injection), and support user acceptance testing (UAT).
• AI Agent Implementation: Deploy AI agents into production with appropriate guardrails, human-in-the-loop controls, and logging, and monitor their quality, usage, and cost over time.
• Business Impact Measurement: Measure the business value of deployed models and AI solutions by defining success metrics, setting baselines, and tracking actual results (e.g., lift, loss reduction, efficiency gains) against expected outcomes.
• Stakeholder Partnership: Work with data scientists, model owners, technology teams, and lines of business to understand requirements and deliver reliable, scalable solutions.

Required

Qualifications

• Bachelor’s degree in data science, Engineering, Mathematics, Statistics, or a related quantitative field.
• Build scalable frameworks and reusable components for developing, integrating, testing, and deploying ML models and AI agents across the data science lifecycle
• 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 Cloud Platforms: 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.

Preferred

Qualifications

• Banking, financial services, lending, or risk management experience.
• Familiarity with model governance, model monitoring, and production support processes.
• Understanding of data engineering and enterprise data ecosystems

PNC is an in-office company that fosters a supportive culture where employees can thrive and achieve balance. We encourage candidates to connect with their recruiter and hiring manager to understand workplace expectations and ensure the role aligns with their goals.

PNC will not provide sponsorship for employment visas or participate in STEM OPT for this position.

Job Description

• Performs complex quantitative analyses and models development to support decision-making by running quantitative strategies.
• Develops new model frameworks by supporting the line of business. Refines, monitors, and validates existing models. Conducts on-going communication with model owners and model developers during the course of the review. Works with large data to create models.
• Performs advanced qualitative and quantitative assessments on all aspects of models including theoretical aspects, model design and implementation as well as data quality and integrity. Reviews reports and associated quantitative analysis. Validates existing models and assesses model risks.
• Evaluates identified model risks and reaches conclusions on strengths and limitations of the model.
• Prepares and analyzes detailed documents for validation and regulatory compliance, using applicable templates.

PNC Employees take pride in our reputation and to continue building upon that we expect our employees to be:

• Customer Focused - Knowledgeable of the values and practices that align customer needs and satisfaction as primary considerations in all business decisions and able to leverage that information in creating cust