Data Scientist at PNC

Compensation

$86,250 – $172,500/yr

Setup: Hybrid
Location: Pittsburgh, Pennsylvania · Wilmington, Delaware · +4
Type: Full-time
Posted: Jun 26, 2026

Description

PNC is seeking a Data Scientist to join their Anti-Money Laundering Analytics & Modeling team. The role involves utilizing statistical techniques to build models for detecting, monitoring, and preventing suspicious account activity. Responsibilities include analyzing datasets, identifying risk indicators, recommending strategy improvements, and leading innovative AML projects. The ideal candidate will collaborate with stakeholders, communicate analytical results to executives, and contribute to developing data-driven solutions.

Required Skills

Statistical Techniques
Logistic Regression
Clustering
Gradient Boosting
Neural Network
Machine Learning Algorithms
Data Mining
Data Preparation for ML Models
Statistical Methods and Tools
Experimental Design
Probability Theory
Sampling
Building, Scaling, and Optimizing Machine Learning Systems
Python
PySpark
R
SQL
Model Validation
Cloud Platforms
AWS
Google Cloud
Azure
AI/ML Models
Analytical Thinking
Competitive Advantages
Data Analytics
Data Science
Machine Learning (ML)
Data Architecture
Disruptive Innovation
Information Capture
Query and Database Access Tools

Additional Information

Required Previous Experiences: 3+ years of relevant/direct industry experience.
Preferred Qualifications: Master's degree or higher in a quantitative field.
Experience with data mining and data preparation for ML models.
Proficiency in statistical methods and tools, including experimental design, probability theory, and sampling.
Expertise in building, scaling, and optimizing machine learning systems with industry recognized ML frameworks and algorithms.
Strong programming skills in Python, PySpark, R, and/or SQL.
Experience working with model risk governing bodies in model validation, and with model implementation partners in productionizing a model.
Critical thinking and problem-solving aptitude with the ability to apply analytical rigor to complex business problems.
Ability to present complex technical concepts clearly and effectively to non-technical stakeholders and business partners.
Ability to manage multiple projects simultaneously.
Strong teamwork skills and ability to work across different departments.
Experience in banking/financial services.
Hands-on experience building various types of AI/ML models, including neural networks.