Quant Analytics & ML Ops Consultant

PNC; Tysons (VA); On-site; USD 91,000 - 203,000; Full time

Benefits offered by this job

Medical benefits

Dental and vision

401(k) with match

Pension and stock purchase plans

Paid holidays and vacation

Job summary

PNC Financial Services Group, Inc. seeks a Quantitative Analytics & Model Development Consultant to design, develop, and operate scalable data, AI, and ML solutions in a production environment.

You will partner with data scientists, tech teams, and business stakeholders to ensure reliability, security, and operational efficiency. The role focuses on ML model deployment, scalable data engineering, and building automated pipelines across cloud and IaC tools, with a strong emphasis on MLOps

Qualifications

• Bachelor’s degree in data science, engineering, mathematics, statistics, or related field.
• Experience supporting ML, analytics, DevOps, or MLOps environments.
• Experience deploying analytics/ML solutions in enterprise environments.
• Strong communication and collaboration skills.

Responsibilities

• Deploy ML models into production with packaging, testing, and deployment.
• Build and maintain CI/CD pipelines for model training, scoring, deployment, and retraining.
• Develop scalable data pipelines and data/feature products for model inputs.
• Create model monitoring and analytics frameworks for multiple models.
• Collaborate with data scientists and stakeholders.

Skills

Python

SQL

R

PySpark

AWS

Azure

ML workflows

Communication

Cross-functional teamwork

Education

Bachelor's degree in data science, Engineering, Mathematics, Statistics, or related field

Tools

Cloud platforms

Azure

Job description

PNC Financial Services Group, Inc. seeks a Quantitative Analytics & Model Development Consultant to design, develop, and operate scalable data, AI, and ML solutions in a production environment.

You will partner with data scientists, tech teams, and business stakeholders to ensure reliability, security, and operational efficiency. The role focuses on ML model deployment, scalable data engineering, and building automated pipelines across cloud and IaC tools, with a strong emphasis on MLOps